ameneh khadivar

Today, the emergence of digital technologies has significantly changed the tourism industry, and virtual reality (VR) is considered one of these modern technologies. Despite its great potential, virtual reality technology has not yet been widely used in Iran's tourist destinations. Therefore, this research aims to examine the opportunities and threats of using VR technology in electronic tourism in Iran. Considering the importance of the tourism industry in the economy of countries and the impact of VR technology on the active organizations in this industry, the present research aims to identify and prioritize the opportunities and threats of using VR technology in the Iranian tourism industry. This objective has been achieved using a mixed research method in two qualitative and quantitative parts. In the qualitative part, the grounded theory method was used, and in the quantitative part, the inferential analysis method was employed. The qualitative sample included 10 experts, managers, and professors, and the quantitative sample included including university professors, managers of the Cultural Heritage, Handicrafts, and Tourism Organization of Tehran Province, and managers of travel agencies. Based on the results, the identified opportunities are ranked as economic, technological, social, and political, respectively, with the variable of experiencing the destination before travel being the top priority among economic opportunities. The threats are ranked as social, economic, and technological, respectively, with the top priority among social threats being the limitation of friendships and intimacy that occur in traditional tourism.

In today's competitive markets, creating strong brand equity is essential for a company's financial success. The existing literature has not yet identified the influence process and influence of brand image on brand equity. Therefore, it is very important to identify the potential factors of brand equity and the relationships between them, such as brand attachment, brand attitude, and brand image. Decision makers can pay more attention to these stimuli to strengthen the special value of the brand, so this research aims to develop knowledge related to brand identity, brand image, attachment, attitude, trust and commitment to the brand and to investigate its impact. Research on brand equity in the household appliance industry and providing solutions to increase brand equity were carried out. The home appliance industry was chosen because it is very attractive to middle-income developing countries. Also, in this study, a conceptual model has been presented that shows the relationships between customers' perceptions of brand image and identity and brand equity.

research philosophy; Affirmative, objective; descriptive, orientation; Application and strategy; is navigation. The statistical sample of consumer research is an Iranian brand active in the home appliance industry. A questionnaire was used to collect data. From Morgan's table, 400 people were determined as the sample size, and then the online questionnaire was sent to the target audience. The sampling method used in this research is simple random sampling. In order to analyze the data and test the research hypotheses, the methods of inferential statistics, structural equation modeling and

Meeting the needs of shareholders and investors, generating profit and income, and covering current expenses are the primary objectives of any economic entity. Banks and financial institutions are no exception, and calculating and understanding costs is one of their most critical activities. In today's competitive environment, organizations must adopt strategies that enhance the quality of services and products while reducing their costs. To achieve this, having tools for analyzing related costs and formulating cost policies for services and products is essential. Companies that previously used traditional costing systems have had to transition to newer, more dynamic, and flexible costing systems that account for various costs, including products, activities, distribution channels, and customers, reflecting the complexity of modern business and production processes. This article presents a model for calculating the cost of banking services using activity-based costing.

The research described in this article is developmental-applied. Given the various factors affecting costs and their nonlinear and cyclical effects, which render banks as complex systems, the methodology of system dynamics was used for modeling. This research is descriptive in terms of data collection methods, which included library studies reviewing sources and references on activity-based costing and systems dynamics principles, simulations concerning the cost of banking services using systems dynamics, and other sources such as books, magazines, articles, and theses from reputable libraries, information search centers, and credible websites.

 In this study, detailed models for two products, mudarabah and deposits, were developed. Cause-and-effect diagrams and their rates and statuses were drawn, and after testing the models, proposed scenarios were designed and compared. Four scenarios were suggested to improve system behavior: improving productivity, adjusting human resources, re-engineering business processes, and automating processes. To select the best policy—one that achieves the most significant cost reduction over a similar period—all policies were compared. The study's findings indicate that the policy of mechanization and process re-engineering is the most effective. Therefore, the bank's general policy for reducing product costs can be a combination of re-engineering and mechanizing processes.

Considering the dynamic business environment and banks as complex systems where many variables influence each other, the systems dynamics approach was employed to model the cost of banking services. The simulation results show that a combination of process re-engineering and automation can serve as an effective policy for reducing the cost of banking services.

Due to the significant and increasing expansion of online shopping in the retail industry and changing consumer behavior, one of the concerns of online retailers in this industry is to create loyalty and a sense of belonging among customers. The aim of this research is to provide a dynamic model for developing a brand loyalty strategy in the online retail industry. For this purpose, using the system dynamics method, the main factors affecting brand loyalty, recognition, and their relationships were determined in the form of a cause-and-effect diagram. Finally, using the cause-and-effect diagram and referring to the opinion of experts and the background of the research, a flow diagram was prepared. The dynamic simulation of the research has been done using Vensim software version 7.3.5 in the time horizon of 10 years, from 1392 (2013) to 1402 (2023) for online retail consumers. The validity of the model has been confirmed through the implementation of behavior reproduction tests and model sensitivity analysis. Then, three scenarios of a 70% increase in customer satisfaction, a 30% increase in customer satisfaction, and a 50% increase in improvement policies and strategies were proposed based on the opinions of experts, and the results of these scenarios were simulated. The findings showed that increasing customer satisfaction and improvement policies and strategies increase brand loyalty, which indicates business growth. Among these three scenarios, a 70% increase in customer satisfaction had a greater impact on brand loyalty. Therefore, businesses with the two arms of customer satisfaction and improvement policies and strategies can increase and control brand loyalty, which lead to an increase in the annual revenue of each customer.

Performance-based budgeting (PBB) aims to allocate public budget resources to organizations based on their performance. Consequently, the necessity of evaluating entities' performance in implementing and executing a performance-based budgeting system has spurred some researchers to expound PBB maturity models, while others have concentrated on designing models to gauge its maturity level. In this investigation, mindful of the developed model of performance-based budgeting in Iran, endeavors have been made to fashion a data envelopment analysis (DEA) model in harmony with the conceptual framework of PBB maturity. Considerations such as the network structure of the PBB maturity model, quantitative-qualitative data, and the hierarchical structure of indicators and sub-indicators within the PBB maturity model should be factored into the design of the mathematical model. Consequently, the primary aim of this article is to devise a two-stage-multilayer fuzzy network data envelopment analysis model that encompasses all three of these considerations and delineates the level of budgetary maturity based on performance for various entities. The outcomes indicate that taking into account the uncertainty present in the data and adjusting levels in optimistic, pessimistic, and moderate, while bolstering the discriminative power of the model, will furnish decision-makers with more precise information.

Green information technology is in the spotlight for organizations, helping them save money by using information technology (IT) to achieve the highest efficiency and thus reduce environmental impacts. One of the ways that can help organizations planning for deploying green IT is to evaluate green information technology maturity (GITM). Previous studies have referred to various criteria for green IT evaluation, most of which are qualitative criteria that are difficult to measure and evaluate in ambiguous conditions. The main objective of this study is to identify crucial criteria that affect the GITM level and to design a fuzzy inference system to assess the GITM level in any organization. While using a Mamdani Inference system, inputs can be verbal expressions or crisp values, and the output shows the level of maturity of green information technology. Since green IT knowledge is not modeled in previous studies, modeling it in the current study is a valuable step for organizations confused about various factors they should consider for going green. The main system criteria are the conditions of the data center, office environment, work practice, procurement, and corporate citizenship. Due to the generality of the model used for the knowledge base system development, organizations can use this system for the green IT maturity level determination. The presented inference system helps organizations understand their status of being IT green and plan for the following steps to accomplish their desired maturity level. The proposed inference system has been tested, validated, and used to determine the maturity level of Tehran municipality.

The goal of any legal system is to develop social order and justice for all through solving the disputes of individual members of society. It seems that with the development of new technologies, reaching this goal can be pursued more quickly. The two leading legal systems in the world, the Roman-German legal system with the application of codified regulations and the common law system relying on precedents, are the source of inspiration for many countries' legal systems. In this research, with the look at the experiences of the Chinese judicial system in the use of artificial intelligence, which has been using the descriptive analytical research method and library tools, we are looking for an answer to the question of whether machine learning tool based on finding patterns in similar decisions is used, will the civil law system lose its traditional framework and in some way integrated into the common law system? It seems that the subordinates of the civil law system can achieve convergence with the common law system by maintaining their principles without losing the roots based on civil law.

In recent years, the rapid growth of virtual space has made people devote more of their time in virtual space, especially to social networks, which can be attributed to the remarkable features of virtual space; including increasing the speed of information exchange, easy and free access to information and variety of knowledge topics. In this regard, the opinions recorded by users in virtual networks have grown day by day and have become very important, and extracting the opinions and feelings of users' opinions for more informed decision-making is of great help to businesses, on the other hand, virtual reality technology in the past few decades It has undergone technical changes and improved immersion and the feeling of remote presence; This technology is used in various fields such as education, tourism, health, sports, entertainment, architecture and construction, etc. The increasing progress of virtual reality technology has caused many businesses to operate in this field, but due to changes Continuous market and the need for timely information, companies should use differentiation and growth strategies, in this regard, they need to ask users' opinions and in line with that, try to grow and improve their business, considering that Users' comments are textual, and reading and summarizing them is time-consuming and difficult. Based on this, the aim of the current research was to categorize comments related to virtual reality technology using machine learning methods and a dictionary-based approach. Therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler, and after data preprocessing, 480,432 samples remained in the data, then Dirichlet's hidden allocation topic modeling was implemented on the data. This modeling separated different topics by examining the distribution of words in tweets; The tweets whose distribution of words were similar were placed into a topic and the number of topics with the highest coherence score was selected, the number of topics 9 had higher coherence and the data were grouped into 9 topics, so once again the Dirichlet hidden allocation modeling was set to 9. The topic was done, with this the tweets were grouped into 9 different topics. To evaluate the model, considering that we had a probability distribution, the confusion criterion was used, the value of which was -9.44, and the coherence score was used for the degree of semantic similarity between words and the distinction between subjects, and the result was 0.47. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technology", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse", and "Indiegame", the topics were analyzed with the help of several graphs. We found that the number of neutral comments on topics such as "New Technology" and "Metaverse" is more than positive and negative comments, which indicates the lack of sufficient information or the lack of use of these technologies, and it is necessary for businesses in this field, to try more in this regard, in the same way, if we observe the graph of "Virtual Games" and "Technological Business", we can see that it changes almost with the same ratio in different years, in the sense that this The two graphs are related, in fact, businesses should keep in mind that the factors affecting these two issues are the same, but users pay more attention to the issue of "Virtual Games", as a result, if the creators of "Technological Business" Focus specifically on "Virtual Games", they will grow more due to the more attention of users, also the creators of games should consider that "Virtual Games" are a topic of more attention than "Indiegame". Is. In the subjects of "Education" and "Gadget", users lost their attention to these subjects in the field of virtual reality over time, in fact they showed their attention to other subjects, so it is better for businesses that operate in this field to take measures To advertise and attract users or change their user area if there is no growth.

Constant changes in the market and the need for timely information force companies to use differentiation and growth strategies appropriate to the needs of customers. (Sánchez, Folgado-Fernández, & Sánchez, 2022). Companies can check and analyze their customers' opinions through microblogging sites (Facebook, Twitter, etc.) and finally improve the desired products or services (Ahmad, Aftab, Bashir, & Hameed, 2018). Today, users express their opinions and feelings and review products in online social networks. Therefore, user comments and the analysis of these comments have become a valuable resource for businesses (Kim et al., 2015; Loureiro et al., 2019).
Virtual reality and augmented reality have undergone technical developments in the past few decades and have improved immersion and the feeling of remote presence. Several examples of applications of such techniques can be found in stores, the tourism industry, hotels, restaurants, etc. (Loureiro, Guerreiro, & Ali, 2020). Due to the constant changes in the market and the need for timely information, companies should use differentiation and growth strategies, nowadays, due to the rapid evolution of the Internet, instead of collecting their opinions through time-consuming and expensive methods such as questionnaires and interviews, etc., they express in the context of social networks, which is very useful for businesses in their development, and they can measure the feelings of customers towards products and services, and understand the needs of users, and finally make appropriate and appropriate decisions in the direction of adopt growth, but in order to use the produced content correctly, text mining and sentiment analysis techniques should be used, which has not been researched in Iran so far. Analysis of users' opinions and feelings about virtual reality technology can help businesses that operate in the field of metaverse, virtual game production, virtual education, virtual tourism, etc., to make better decisions and plans.

Social media generates a large amount of real-time social signals that can provide new insights into human behavior and emotions. People around the world are constantly engaged with social media. (Al-Samarraie, Sarsam, & Alzahrani, 2023).
On the other hand, the amount of data is increasing day by day. Almost all institutions, organizations and business industries store their data electronically. A huge amount of text is circulating on the Internet in the form of digital libraries, repositories, and other textual information such as blogs, social media networks, and emails (Sagayam, Srinivasan, & Roshni, 2012).
Topic modeling is one of the most powerful techniques in text mining for data mining, discovering hidden data and finding relationships between data and textual documents (Jelodar et al., 2017).
The technological advances of the last century have confronted societies with new realities that have indisputably improved daily life, making it more convenient and interesting. In recent decades, technology using virtual reality and wearable devices have had a significant impact in the fields of education, tourism, health, sports, entertainment, architecture and construction, etc. (Kosti et al., 2023).
Virtual reality is a technology that allows a user to interact with a computer-simulated environment, whether that environment is a simulation of the real world or an imaginary one. With virtual reality, we can experience the most frightening and overwhelming situations with safe play and a learning perspective (Mandal, 2013). Most people are curious about the possibilities and future of new technologies, considering the various applications it is supposed to offer such as virtual meetings, learning environments and many others, however, there are also concerns about potential negative effects. because real world signals can be transmitted in the virtual world. In this regard, people express their feelings in different social networks (Bhattacharyya et al., 2023).

According to the main goal of the research, which is to classify comments related to virtual reality technology using machine learning methods and a dictionary-based approach, therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler and After data preprocessing, 480,432 samples remained in the data, then Dirichlet hidden allocation thematic modeling was implemented on the data. By examining the distribution of words in tweets, this modeling tries to separate different topics by detecting the distribution of words; The tweets whose distribution of words are similar were put into a topic, and the number of topics with the highest score was selected, the number of topics 9 has higher coherence, and the data was grouped into 9 topics, so once again, Dirichlet hidden allocation modeling was applied 9 topics were done, whereby the tweets were grouped into 9 different topics. Considering that we have a probability distribution, the confusion criterion was used to evaluate the model. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technologies", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse" and "Indiegame" were named.

In this research, by examining topics in different years, we observed that the topic of "Progress" was the most popular topic among users from 2017 to the end of 2021, in early 2022, this topic gave way to "Metaverse", currently "Metaverse" is one of the most popular topics being discussed by users. Businesses in the field of virtual reality should strive for the attractiveness of "Metaverse" and attract users. Likewise, if we observe the "Virtual Games" and "Technological Business" graphs, we can see that they change with almost the same ratio in different years, meaning that these graphs are related to each other, in fact, business and keep in mind that the factors affecting these two issues are the same, but in the case of "Virtual Games" it has more effects, and if "Technological Businesses" specifically focus on virtual games, they will grow more due to the greater attention of users. had Similarly, "Indiegame" which have had a series of changes but in recent years have had a declining trend and then no change, now the creators of these games should check, and in general "Virtual Games" are a more interesting topic than "Indiegame". In the subjects of "Education" and "Gadget" it has been decreasing since the beginning of 2017, which shows that users lost their attention to these subjects in the field of virtual reality over time, in fact to other topics showed their attention, so it is better for businesses that are active in this field to take measures to advertise and attract users, or change their user field if there is no growth.

Agricultural Insurance Fund has a strategic and fundamental role in managing the risk of the country's agricultural sector. Although the management and transfer of an important part of agricultural risks is currently done by this fund, but the agricultural insurance system, structure and scope of authority of this group is not commensurate with the needs of today and the level of dissatisfaction among insurers and all stakeholders of this organization is high. Preliminary studies show that the current agricultural insurance system, fund structure, missions and strategies, financing methods, rules and regulations all need to be changed and revised. Due to the wide range of issues and the key role of the Agricultural Insurance Fund, in order to improve performance and improve services in providing services to the community of insurers, the strategic renewal of the fund was on the agenda of managers. The purpose of this study is to provide a comprehensive model for strategic renewal in this organization. In order to achieve the model, grounded theory approach has been used. The research tool is semi-structured interviews with experts and stakeholders of this fund throughout the country . Both manual and software analysis have been used to analyze the interview data. The final paradigm model shows the factors affecting the strategic renewal of the fund, the intervening and contextual conditions affecting this renewal, and finally the strategies and results of the modernization that are presented in the form of concepts and categories

The insurance industry is vital in the country that is affected by green marketing. However, there is no accurate information on the green marketing situation in the country's insurance industry. This research has been tried to identify the main criteria of green marketing evaluation to provide a model for a green marketing audit. In this regard, first, the main criteria were identified and approved by experts through a questionnaire. The final framework consists of 24 sub-criteria that are classified into ten criteria. Then, by using the fuzzy DEMATEL method, the internal relationships between the criteria and the hierarchical analysis method, each criterion and sub-criteria's final weight are obtained. According to the results, among the causes, the criterion of mission has the greatest effect on all criteria and takes the least effect from other criteria. So, this criterion has the most stimulus role, and by optimizing it, we can expect the optimization of other factors. The green sales factor is also strongly influenced by other factors and is optimized by optimizing other factors. Also, the criterion of the mission with an effective weight of 0.23 is the most important, and the criterion of green design with an effective weight of 0.024 is the least important

The pressure on Chief Information Officers (CIOs) to focus on Green Information Technology (GIT) has recently increased. However, many organizations still need to manage their environmental efforts due to a lack of strategic planning. This study aims to design a fuzzy expert system to suggest a proper GIT strategy for organizations, considering qualitative, obscure, and fuzzy factors. The study employs questionnaires distributed to experts to evaluate the factors that impact the level of GIT maturity using fuzzy logic. Based on the results obtained, a fuzzy expert system is designed to suggest a proper GIT strategy. The proposed expert system has been used at Alzahra University as a case study for selecting GIT strategies and activities. The findings suggest that Alzahra University should adopt a primary-level GIT strategy involving a three-year process of implementation, which includes actions such as removing old monitors, developing a green supply chain, reengineering organizational processes, disposing of useless ICT equipment in a green manner, and adopting green marketing practices. The fuzzy expert system designed in this study can help organizations adopt appropriate GIT strategies.

Assets and debts management includes a series of technical tools and methods that contemplate risk control and asset value creation for shareholders. Since one of the main tasks of banks' financial management is asset and liability management, therefore, in order to increase their profitability, they use asset and debt management techniques to control risk, in order to minimize the losses caused by their transactions. In this research, to solve this challenge, an expert system was designed. The study period of this research was between August 2017 and December 2018. First, data mining techniques were used to predict the bank's credit risk due to the data of 311 legal clients of the Tosee Saderat Bank, with 44 Attributes along with a labeled variable (background of fulfillment of commitment). Data mining was done in two phases, the first phase was data mining in Python language and the second phase was done with Rapid Miner software and creating a decision tree, and finally the finalized rules for measuring the bank's credit risk. Then, the variables and proportions affecting the balance of assets and liabilities of the balance sheet items were collected by interviewing an expert and ranked by five experts in terms of the importance of scoring, and then weighting was done by ARRAS method. High-weight variables (subsystem I) entered the expert system along with the variables in the rules of the decision tree (subsystem II). Finally, the validation of the expert system was obtained. In this way, the index of the ratio of working capital to total assets and the history of working with the bank are the most important variables in validation methods. According to the obtained results, if the credit risk is high, but the balance sheet items are in the optimum, the resources and expenses will be in balance,....

In recent years, with the development of machine learning and big data technology, user data has become an important element in the production processes of companies. By applying data mining approaches to customer data, organizations understand customers' behavioral patterns, their needs, and the hidden relationships in the data, and based on these patterns, they can better use their resources to meet customer needs. Clustering is one of the data mining techniques used to group customers according to their different characteristics. The main goal of this research is to cluster customers based on LRFMSP indicators and finally classify them and predict their buying behavior using decision tree (DTC), multilayer perceptron (MLP) and support vector machine (SVM) classification techniques. The study was conducted on 387,496 transactions from customers of a retail store in Western Europe between February 2018 and February 2019. Each transaction attributed to a customer is part of an individual's behavior that is modeled on a set of transactions to shape the customer's purchasing behavior. Performing K-means++ clustering and determining the optimal K led to identifying three clusters for customers. Also, testing and checking the classifiers showed that the MLP classifier with one hidden layer and six neurons in this layer would be the most accurate and the DTC classifier is the fastest among the classifiers reviewed. Examining the behavior of cluster customers showed that customers can be divided into three categories: loyal, potential, and lost.

The main goal of the current research is to predict the ethical purchase intention of sustainable products in the circular business model through the behavior of customers/tourists using artificial neural network (ANN). The statistical population of the present study are customers/tourists who have used the products/services of Gargop restaurant. Sampling was done based on Morgan's table and 384 people were examined through a questionnaire. The results indicate that as much as the input variables include attitude (willingness to pay more money, attitude towards circular business models), perceived value (social value, functional value and value) and awareness (awareness of Ethical product/service and brand awareness) have a higher value, it is expected that the ethical purchase intention will be stronger. The present study by examining the behavioral factors of customers to the collection of literature related to circular business models and also Sustainable development helps.

With the emergence of social media, people share their ideas, experiences, opinions, and intentions on Twitter, Facebook, and other platforms. Analyzing and reviewing these opinions on social media gives organizations useful information about the market, customers, and competitors, the purchase intention of users, which for commercial can be used such as purposes like targeted marketing and advertising depersonalization. This research aims to provide a system based on artificial intelligence to predict the purchase intention of users on Twitter. To conduct the research, 23,000 English tweets of users were collected and their sentiments were analyzed using the vocabulary-based method, yielding acceptable results with an accuracy of 0.81. The results of sentiment analysis showed a positive public sentiment about the iPhone 13. The purchase intention of Twitter users was extracted using a long-short-term memory deep convolution neural network with an accuracy of 0.81, and then a model for predicting purchase intention was presented using sentiment analysis and features extracted from tweets. To measure the effectiveness of users' emotions and static features, a multi-layer perceptron deep neural network model was used, and the results showed an accuracy of 0.80. Using this model to market managers plan properly to identify their customers and reduce marketing costs.

Due to the limited power of human perception of the outside world and also the power of comprehensive and deep reasoning, he faces uncertainty and uncertainty. Multi-criteria decision-making methods, including methods to increase confidence and reduce uncertainty due to judgment Decision makers and comprehensiveness of information. One of the indicators calculated in the decision matrix in conditions of uncertainty is the minimum regret index. The purpose of this study is to identify options with the least regret in selecting faculty members. Therefore, using the variables obtained from the previous literature and the opinion of experts in the field of study, an expert system was designed to assist in decision-making in attracting faculty members.The results of the analysis indicated that the options of academic record, ethics (communication skills), teaching ability (ability to express and convey content) and decision-making skills are indicators that have the least risk of being considered in the scientific selection of individuals. According to the results of this research and the designed expert system can be used to help decision-making in attracting faculty members of universities by reducing time, increasing the quality and accuracy of decision-making.

In line with increasing attention to the scope of Business Process Management (BPM) over the past two decades, many techniques and tools have been introduced. Finding the proper technique and tool in each phase of the business process management life cycle takes time and effort. This study aims to design and develop an ontology to facilitate the selection of suitable techniques and tools at each step of the BPM life cycle. This ontology provides a common understanding of concepts of this domain for computers. The study results showed that two taxonomies for techniques and software tools for business process management were created based on BPM life cycle steps. Then, an ontology was developed for them. Noy & McGuinness methodology was applied to implement this ontology, and Protégé 5.2 and owl language were used. Also, the quality criteria-based approach was used for the evaluation of ontology. All the main concepts in the domain of BPM techniques and tools were extracted from previous studies. There are 298 terms. 58 of them are domain concepts or classes, 2 are about taxonomic relations, 2 are related to data property, and 224 are instances. This research used these terms, and the deployed ontology with the quality criteria-based approach was evaluated.

The sustainable development of creative tourism, which is the third generation of tourism and fits the needs of current tourists, is a very complex and dynamic system. That is, there are many factors involved in it that influence each other and influence each other, and their relationships are not linear, which can be easily decided and planned. Also, each of the factors involved in this system has a different purpose and management. This can lead to overlapping and even conflict between stakeholders. From the side of the passage of time, this system undergoes a lot of changes and transformations and is not static. Therefore, the aim of the current research is to clarify what important factors affect the sustainable development of creative tourism and what strategies should be used in the direction of the sustainable development of this new type of tourism.

Design/methodology/approach: 

For this purpose, the method of system dynamics has been used.

All the factors involved, including the dimensions of sustainability as well as the dimensions of creative tourism, are drawn in the form of a cause and effect diagram (CLD) and then the related accumulation-flow diagram (SFD) is presented and this model is a basis for Management and planning are used for sustainable development of creative tourism.

In the development of creative tourism, the main goal is to increase the number of loyal tourists. . But in this regard, we should also pay attention to sustainability goals to increase the value of visits for both tourists and the local community and service providers.

Originality/value:

 The present research has been innovative and original and added value by applying the system dynamics approach in the complex and dynamic field of creative tourism with regard to sustainable development in the researches of the tourism industry.

The emergence of big data is a radical shift in the business context, leading to a change in value creation and capture. This phenomenon is a newborn concept in the business and management literature confirmed by the growing number of publications over recent years. This paper presents an updating comprehensive bibliometric analysis to describe and assess the scientific landscape of value creation and capture based on leveraging big data in the literature. Bibliometrix and VOSviewer were selected as software tools for descriptive and network bibliometric analysis based on the Web of Science Core Collection database from 2011 until 2020. By implementing bibliometric analysis such as analysis of citations and co-occurrence of keywords, we have recognized the most prominent and influential authors, papers, journals, countries, and four potential clusters of current trends in studies. These four trends of value creation and capture from big data studies are: 1) strengthening the basic knowledge of value creation in the big data era, 2) data-driven business model and value capturing, 3) dynamic capabilities and centrality of knowledge, and 4) digital transformation of the service industry. Finally, by identifying the existing research gaps, future research directions in each cluster are demonstrated.

Customer Retention and maintaining customer relationships and preventing customers from Churn is one of the most important tasks of organizations in today's highly competitive markets. In this study, the issue of customer churn and customer retention strategies have been investigated. These issues have been studied through systematic literature review and from different angles such as the field of organization, degree of individualization of customer relationship management, customer segmentation, and selection of key customers, employee engagement and evaluation. In addition, a model based on deep learning networks has been used to predict customer churn. As a result, a conceptual framework and model is created based on the existing literature in this field and then combined with the customer churn prediction model using deep learning networks. The results show that the use of deep learning in predicting customer churn is a very effective and efficient way to solve the problem of customer retention and customer churn. This approach is not only able to accurately predict which of the organization's customers are turning away from the organization and disconnecting from the organization, but also can accurately identify the factors and parameters affecting customer churn and bring very valuable insight for the organization.

Considering the key role of the Agricultural Insurance Fund in the Iranian agricultural system by covering the risks and compensating the damages, in order to improve the performance and improve in providing services to the community of policyholders, strategic modernization of the Fund is necessary. The purpose of this study is to identify and analyze the needs and expectations of policyholders as the main beneficiary of the Fund.Achieving this knowledge with a pathological approach has been done through a survey of insured farmers. The research method is descriptive in terms of data and applied in terms of purpose. The research tool was a questionnaire and statistical analysis methods were used to analyze the questionnaire data. In total, 134 valid questionnaires were received and analyzed. The results of the study indicate significant gaps in various fields between the needs and expectations of policyholders and the current state of the Fund. Also, satisfaction with the services provided by the Fund is moderate. Finally, solutions and suggestions are provided to eliminate the identified gaps and, as a result, increase the satisfaction of policyholders, which include various aspects such as product design, compensation, culture building, customer relationship management, enhancing brokers' marketing capabilities, and process digitization. The results of this research can be used in developing a Fund renewal program

Due to the global attention to cryptocurrencies especially bitcoin, governments, banks, and other industries are expected to use cryptocurrencies in their daily transactions. Therefore, as any other financial field, there is a need to identify the challenges in this field to safe investment. On the other hand, by the expansion of social networks, unstructured data is increasing, which can be used to create added value in various areas such as sentiment analysis. Therefore, this study was conducted to investigate the impact of perceived risk by social network users on the price of Bitcoin. According to the research findings, the identified risks in the field of bitcoin include social, economic, security, technological and legal risks., The conversations on the Bitcoin Talk Site were used to extract the bitcoin risks. After collecting these conversations by the web crawler, the conversations were clustered into thematic clusters using the Latent Dirichlet Allocation algorithm, which is one of the most popular methods in Topic Modeling. were analyzed using vocabulary-based method and AFINN dictionary. NARX Neural Network was used to measure the effect of Userschr('39') Sentiment on the price of Bitcoin. The results show a correlation of 0.99 and a mean square error of 0.001, which means that there is a correlation between the actual price and the predicted price of Bitcoin. The findings of this study can attract the attention of financial actors and businessmen in the field of bitcoin to plan a safe investment and reduce risk.

The aim of this study is to identify and prioritize the key factors of success in implementing smart contracts in the insurance industry. The research methodology of this study is descriptive, exploratory, and survey the content of written documents. After identifying key factors based on the study of research literature and interviews conducted by managers and experts in insurance affairs in the area related to the issue were used to evaluate the importance of each factor in field study and questionnaire distribution. Correlation analysis of identified factors and network structure modification was carried out based on fuzzy Dematel approach and ranking the success factors of smart contracts based on blockchain was done using the ANP method approach. With regard to the evaluation of the acceptable form in the first step of screening, the number of 29 identified factors were confirmed. Then using ANP method and evaluation, it was shown that superior management and superior management support with 0.269 weight were ranked first, excellent management support with 0.168 weight and openness were ranked third by operational managers and organizational integration with 0.126 weight.

It is necessary to pay more attention to the main stakeholders of the programs in the insurance industry, especially who are not in the field of strategic plans control, and to use the participation of all stakeholders in reviewing the macroplans of the insurance industry. This study focuses on identifying, categorizing and prioritizing insurance industry stakeholders and examining their needs and expectations.

In the first stage, an online questionnaire was used to categorize the stakeholders, visited by 468 people and finally received 125 answers. The target community is insurance experts and activists. Power-interest and effective-affected models have been used to map the stakeholders of the insurance industry. To extract stakeholder expectations, library studies, web search and sending letters to stakeholders outside the industry, and descriptive structured questionnaires to stakeholders within the industry and deep analysis has been used. The total number of respondents from industry stakeholders to the online questionnaires was 341 who answered the questions of this research descriptively.

Central Insurance, the Ministry of Economic, legal policyholders, claims assessors, InsurTechs, insurers' syndicates and brokers are key players in the insurance industry.

The key stakeholders have abundant sources of power to influence the organization and therefore, should be in the center of attention and strategies to meet their needs and expectations should be considered and included in all developments and decisions. Quantitative and qualitative analysis and prioritization of the total identified strengths, weaknesses, opportunities and threats can be used to extract strategies and programs to interact with them

With the development of mobile communications and the advent of the Fifth Generation, increasing Internet speed and capabilities as enablers of the Internet of Things is creating changes in people's daily lives and work. On the other hand, there are environmental issues, the possibility of pathogenicity, and doubts about the realization of the imagined features, which are much debated today. Most researches has paid attention to technical and developmental dimensions of it, while from the social science perspective, the acceptance of new technology and from the marketing perspective, consumer satisfaction can be important for service providers. Accordingly, the purpose of this study is to investigate the perception of Twitter social network users as a microblogging platform about the fifth generation of mobile communication using machine learning methods and Sentiment Analysis .Therefore, a collection of more than 40,000 tweets on this topic was compiled through the Twitter user interface and the Recurrent Neural Network model was created with79% accuracy. Finally, the topic modeling was done by LDA method for further deepening. The results shows that although there is dissatisfaction with the quality provided, the cost and coverage of the fifth generation of mobile communications, health concerns, rumors that Covid19 is related to the fifth generation, but still more people are optimistic about the future of this technology in various fields such as the Internet of Things and artificial intelligence..