رضا رادفر

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.

Todays, tax revenues play an important role in financing governments. The issue of increasing tax revenues and possible ways for this increase is an attractive issue for governments. Especially in a single-product economy like Iran, whose main source of income is the volatile and unpredictable oil income. This research aims, while briefly introducing the presented models and classifying them, effective factors in increasing tax revenues and evaluating public trust and participation in the field of taxation have been examined and identified with a meta-integration approach, and the main components of this model include legal factors, social factors, there are cultural factors, managerial factors, political factors and managerial factors. Then, in order to better understand the variables affecting this relationship and understand the existing dynamics, using the approach of system dynamics modeling and drawing causal-loop diagrams (CLD), a model for evaluating trust and public participation in the field of taxation is presented and finally simulated so It was done by Vensim software. The simulation results indicate the positive effect of economic factors on public trust and participation in the tax field, with the point that the effect of economic factors is greater than the simultaneous application of all components.

Today, knowledge-based companies play a very important role in achieving advanced technologies and producing innovative products, and as a result, the growth and development of countries. The success of these companies is due to the development of advanced research and development activities that have a high level of technological complexity.

The current research is of mixed type. In the qualitative part, the grounded theory approach was used and the data was collected using interview tools and literature review. Quantitative part data was collected from the desired statistical population through a researcher-made questionnaire, then data analysis and model fitting with structural equation modeling method and using smart software pls3 done.

91 effective factors on determining the level of technological complexity in the form of six categories were identified and the presented model was confirmed.

The model for determining the level of technological complexity of research and development activities resulting from the present study can be a suitable tool for managers of research and development departments of knowledge-based companies in order to improve the level of research and development activities based on global standards and creating transformation in the fields of technology and knowledge production.

As oil and gas resources play an essential role in energy, oil and gas industry technologies have also developed rapidly in recent years, such as smart drilling technology, smart oil and gas transmission pipelines, and digital offshore platforms. The oil and gas industry has four aspects: business, management and decision-making, monitoring and cyber security. Finally, the state of application programs, the level of understanding of block chain in the security of the Internet of Things in the oil and gas industry, opportunities, challenges and risks and the development process are examined. Therefore, the main goal of the research is to provide a model of block chain mechanisms for the security of the Internet of Things in the Iranian National Oil Company. The nature of the current research is exploratory and qualitative data is used in it. The statistical population of the research is the experts and professors of doctorate in management and information technology in the oil and gas industry (NISOC). In this context, the researcher used the snowball method to achieve theoretical saturation, and the researcher achieved theoretical saturation after structured interviews with 10 elites and experts. Data analysis was done with the qualitative method of Foundation Data Theory and Maxqda2020 software. The findings indicate that the causal factors affecting the block-chain mechanisms for the cyber security of the Internet of Things in the oil and gas industry in the form of three concepts, including data aggregation and storage (network), validation and identity management and key management and data encryption were identified. Also, influential background factors include nine concepts of financial resources, management, distribution of infrastructure services, data confidentiality, system stability, scalability, accessibility, transparency, expertise. and intervening (environmental) factors include seven concepts of environment. Operational standards, standards and procedures are the nature of data for mechanisms in various industries, social environment, cultural environment, government laws and regulations, political relations and the strategies of block chain mechanisms for the cyber security of the Internet of Things, in the form of six concepts of electronic health, creating a secure layer, smart transportation, smart networks, the use of smart block chain contracts, the design of industrial Internet of Things (IIOT) devices, and the consequences And the results of the implementation of block chain. Mechanisms for the cyber security of the Internet of Things in the oil and gas industry of the National Company of the Southern Oil Rich Regions(Nisoc), including the attitude of reducing production costs, high capacity for storage, intelligent field, organizational agility, environmental dynamics, security, System accessibility, improvement of the user interface, the quality of information (distribution and variety of information) and the speed of data processing.

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 decision-making 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 chemical industries in order to determine portfolio selection. The main advantage of proposed approach is help an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge.  

According to the studies conducted on New Technology-Based Firms (NTBFs), the biggest problem of these companies is their access to financial resources. Studies show that among the indirect intervention policies, credit guarantee schemes have been considered by governments for facilitating the financing of these enterprises due to the need for less budget and a higher leverage effect. The purpose of this article is to present the appropriate credit guarantee model for new technology-based Firms.

In this study, which used a combination of qualitative and quantitative methods, firstly, by reviewing the literature, credit guarantee models, key components, and main criteria for choosing the appropriate credit guarantee model were identified and validated by the opinion of an expert group. Finally, the most appropriate model was selected using the Multiple Criteria Decision-Making Model and Analytical Network Process (ANP). Also, to form the grounds for policy implications for credit guarantee programs in Iran, an in-depth study of credit guarantee programs in the country and a comparison of the constituent components of credit guarantee programs with the classified models for credit guarantee in the world was carried out.

The findings of this study indicate a lack of effective policy formulation and a deficiency in the precise and targeted selection of a local credit guarantee model for emerging technology-based firms in Iran. The central outcomes of this study revolve around the identification of credit guarantee models, the delineation of key components, and the establishment of criteria for choosing the most suitable credit guarantee model. Furthermore, the research entails the ranking of both these criteria and the various models under consideration. According to the insights gathered from experts in this domain, the research findings highlight the primary criteria for selecting a credit guarantee model. These criteria, ranked in order of significance include: facilitating the provision of credit services, assessing the consequences of the credit guarantee system, effective risk management, and governance of the credit guarantee system. This ranking reflects the experts' assessment of the most pivotal factors when evaluating credit guarantee programs. The criterion of facilitating the provision of credit services has the most weight (importance). Also, policy implications such as: "Government support policies regarding the development of guarantee tools", " creating a centralized and reliable credit risk database" and "Governance and functional changes in the country's credit guarantee institutions" were proposed to create a local credit guarantee system for NTBFs.

In this research, utilizing the ANP methodology, the selection of an apt credit guarantee model for the nation aligns with the specific requirements of emerging technology-based firms, the prevailing economic and institutional landscape, and global best practices and successful models. This approach holds substantial promise for achieving significant success, not only in shaping effective policies but also in their successful implementation within the realm of financing for these companies. Reforming the institutional and functional structure of the country's credit guarantee institutions in the short term, as well as the formation of new credit guarantee institutions according to successful credit guarantee models in the world and their development in the long run can play a significant role in the financing of NTBFs.

This study aimed to develop IoT-based machine learning algorithms care and improvement while detecting and predicting real-time epidemics.The target disease is COVID-19 due to its importance and epidemic.The research method is based on design science. The research approach is forward-looking, so the mechanism of disease transmission and its effective characteristics enable us to make predictions about the disease and thus design disease control strategies and health care.The research was carried out in a seven-step process. IoT features were extracted in the present study with experts' opinions. The features obtained in the experiment of two different algorithms, "k nearest neighbor" and "decision tree," were created on the data to determine the best model.After selecting the best depth validation of the model were performed by confusion matrix analysis.The results of running k-nearest neighborhood and Decision Tree algorithms for the prediction of COVID-19 indicated an accuracy of > 98%. Higher sensitivity (99%) was obtained in the Decision Tree algorithm, which is very important diagnosing COVID-19 and indicates the minimum number of false negatives in the test results.

The purpose of this research is to compile a model of digital marketing strategies in successful Iranian startups. According to its purpose, the research method is practical, and in terms of its implementation, it is mixed (qualitative-quantitative). The statistical population of the research in the qualitative part includes 13 academic and executive experts in the field of startups in the country, who were selected using the purposeful judgment method and the snowball method, and the statistical population in the quantitative part includes the marketing unit of active successful startups. There are 250 people in the country (5 companies) and marketing professors of 5 top universities in Tehran, and the statistical sample was 148 people, 180 distributed and 163 analytical questionnaires were selected using Morgan's table. A semi-structured interview and a researcher-made questionnaire taken from the qualitative section were used to collect information. In the qualitative part, the data theory method of the foundation was used, and the data obtained from the interviews were coded and analyzed in the three main stages of open coding, central coding, and selective coding, and in the quantitative part, the factor analysis of the indicators was done. The results of the qualitative part indicated that 241 primary codes, 46 central codes and 14 selective codes were identified and extracted. The results of the research showed that the causal conditions with 4 variables of network capability, use of artificial intelligence, marketing capability and customer knowledge have an effect on digital marketing strategies, and the results of confirmatory factor analysis showed that the fit indices of the model were confirmed.

This article presents a new model for optimizing the safety risk of take-off, as the most important and dangerous flight process, using a combination of BI algorithm and recurrent neural network LSTM. The goal is to train an effective neural network with past data records of air accidents to predict safety risk parameters. For this purpose, 17 safety features, such as weather conditions, aircraft configuration and preparation, flight information and air traffic were obtained. The data related to 2019 to 2020 was selected after performing exploration, summarization, cleaning, normalization operations with 28813 data records. Due to the dependence of flight data on their previous inputs and the need for a kind of memory, training was performed by deep learning algorithm (LSTM) in Python environment. After learning, the learning error was about 6 percent and the mean square error was about 116/0. It shows that the error percentage is negligible and the proposed model has high validity. Also, this model solved the problem of exploration and cleaning of bulk flight data by having advanced tools such as ETL, metadata and real-time monitoring and was able to predict the most important safety risk factor (speed V1) with high accuracy. This pattern helps the flight service in controlling the important parameters of safety risk, such as the speed of aircraft taking off from the runway, controlling the safe take-off speed and most importantly controlling the loss of flight with a reliable strategy.