درخت حوزه‌های تخصصی

Purpose: This research aimed to identify some of the existing financial frictions in the Iran's digital economy. In particular, based on cases taken from digital and knowledge-based companies, it empirically investigated the importance of the role of base volume in the liquidity of those companies' stocks in Tehran Stock Exchange.Method: To evaluate the empirical implications of applying the base volume in daily stock market practice, retrospectively a quantitative estimate of the base volume was implied by the economic model within the rules imposed by the market regulator via MATLAB software programming. Then, using the Generalized Method of Moments (GMM), the effects of the estimated base volume, percentage of free-floating share, securities turnover, and the ratio of transaction volume to base volume on Amihud index were econometrically studied for the selected companies during the period 2015-2020.Findings: The findings indicate that the applying base volume on the selected digital and knowledge-based companies has had a negative effect on the calculation of the final price and on the liquidity of studied knowledge-based companies. Also, the results of using the machine learning method (decision tree) showed a importance coefficient of 32.6% for the base volume on the Amihud index of the selected companies.Conclusion: Our results suggest that base volume as an idiosyncratic financial friction induced by Iranian stock market regulator has aggravated the illiquidity of studied digital and knowledge-based companies and thereby could have raised the financing costs for those companies. This would ultimately impede those companies’ growth prospect.

Purpose: The current research aims to develop a model for digital transformation within the virtual space of the Broadcasting Organization in accordance with increasing the functionality of the virtual space among the audiences. Due to the lack of a model in this field in order to benefit from it, this research aims to extract the components of the model using the thematic qualitative method or theme analysis.Method: The statistical population includes 15 experts in media management and virtual space. To measure reliability, intra-subject agreement method (reliability between two coders/evaluators) was used to determine the reliability of the texts, and the reliability coefficient obtained for all three interviews and the total reliability coefficient was (0.87), surpassing the minimum acceptable threshold of 0.7, confirming the reliability of the codings and the interviews.Findings: The findings indicate that the model requirements consist of 6 main categories, 9 sub-categories, and 38 sub-categories essential for creating digital transformation and enhancing the interaction of the Islamic Republic of Iran's radio and television with the virtual space. The main categories of the model encompass content production, comprising six main components: content production (with six indicators), opportunities and threats (with 11 indicators), strengths and weaknesses (with 13 indicators), digital transformation components (with five indicators), platforms of introduction (with seven indicators), and consequences of digital transformation (with four indicators), totaling 46 indicators.Conclusion: The results show that establishing a favorable interaction in the virtual space and new media by creating a digital transformation in the national media is effective in attracting the audience and improving the performance of the national media and ensuring the satisfaction of the stakeholders.

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.

Today, users generate various data increasingly using the Internet when choosing a product or service. This leads to the generation of data about the purchases and services of various consumers. In addition, consumers often leave feedback about the purchase. At the same time, consumers discuss their attitudes about goods and services on social networks, messengers, thematic sites, etc. This leads to the emergence of large volumes of data that contain useful information about various manufacturers of goods and services. Such information can be useful to both ordinary users and large companies. However, it is practically impossible to use this information due to the fact that it is located in different places, that is, it has a raw, unstructured character. At the same time, depending on the target group of users, not the entire data set is needed, but a specific target sample. To solve this problem, it is necessary to have a tool for structuring information arrays and their further analysis depending on the set goal. This can be done with the help of various frameworks that use methods of machine learning and work with data. This work is devoted to elucidating the problem of creating means for evaluating consumer preferences based on the analysis of large volumes of data for its further use by the target audience.  The goal of the development of big data analysis systems is obtaining new, previously unknown information. The methodology of application of algorithms of work with large data sets and methods of machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical information, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analysis systems built using specialized machine learning libraries.

The positive effect of social networking, particularly social networking sites (SNSs), on improving the process of learning has been acknowledged by many recent types of research. The relationship between features and characteristics of SNSs and the development of students' social networking was of interest to past researchers. As social networking is primarily perceived as intelligent thought and action in both real and virtual environments, there seems to be a need for a qualitative exploration of the influential factors of students' social networking. The study has been conducted using the case study method to look at the identified factors retrieved from previous research. A semi-structured in-depth interview was used to investigate the viewpoints and experiences of socially proactive and successful students at Iranian universities. Findings explain students' social networking due to three factors categorized as central, causal, and contextual. The personal learning system has a critical position among the various factors affecting students' social networking. Therefore, despite the facilitating role of social networking in promoting the learning process, students' social networking would be useless without utilizing a personal learning system. We can see a dynamic and interactive cycle of learning and social networking in the university context. The research has been founded on critical consideration of previously studied factors affecting social networking that were mainly limited to online technologies according to qualitative exploration. As a result of this research, different learning and social networking levels regarding diverse meaning, function, and complexity were identified.

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences.

Purpose: The growth of Smartphone applications has led to the development and transformation of business sector. The present work aimed to assess factors influencing the intention to use shopping applications.Method: A structural model was formulated for analyzing and testing the existing factors among shopping application users. The statistical population of the research comprised of the users of shopping applications in a public university in Iran. This study employed a questionnaire survey, which consisted of two sections. The first section included general demographic details of the target respondents, while the second section comprised 30 items to measure the constructs of our conceptual model. All items of constructs were adopted from previous literature. A total of 288 questionnaires provided usable data.Findings: The results revealed that factors such as Convenience, Perceived Ease of Use, Trust, and Perceived usefulness affect the intention to use shopping applications, while factors such as Perceived Innovativeness, Perceived Risk, Perceived Enjoyment, and Social Influence were found to be non-influential.Conclusion: This research was conducted based on a comprehensive review of the research literature and identification of influential constructs with the approach of creating an integrated model of factors affecting the intention to use shopping applications. Based on the research results, focusing on ease of use and creating the experience of perceived usefulness along with the use of tools that lead to the improvement of trust is critical for practitioners.

Purpose: This research aimed at presenting the consumers’ compulsive buying pattern through internet advertisements of digital content in Iran's TV shopping industry.Method: Research Methodology was practical in terms of purpose and conducted using mixed method (qualitative-quantitative). The research community was based on the purposeful sampling method, and consisted of ten marketing experts. The research tool was interview. MAXQDA software was used to analyze data through database theory. The statistical population in the quantitative section included TV buyers in Mashhad. Based on Morgan table and random sampling, 384 samples were selected. The research tool was a researcher-made questionnaire, and the Structural Equation Method (SEM) in SmartPLS software was used for data analysis. The validity of the questionnaire was confirmed by using face, content, divergent and convergent validities, and its reliability was also confirmed using Cronbach's alpha. Both of Composite and homogeneous reliability were evaluated.Findings: "appropriate digital marketing mix design for TV sales, digital marketing capabilities, individual demographic characteristics, lifestyle, family " constitute the causal conditions in the consumer’s compulsive buying pattern in the TV shopping. According to the findings, “quick and transient purchase and irrational and emotional purchase” were identified as a central phenomenon. “TV's attractiveness from the audience's point of view, broadcasting policies, sales companies' policies, national TV belief and trust, individual awareness and knowledge about buying products and society's culture” acted as intervening conditions. In the field of buying, “intellectual structures of society and executive structures of society” identified as background conditions. Human strategies and structural and organizational strategies” acted as strategies and “Consumers outcomes; families and society outcomes” were identified as outcomes. According to the results of structural modeling, the relationships of the identified pattern were significant.Conclusion: The issue of compulsive buying is one of the most important and common issues, and buying from TV has fueled this issue, and has become the basis for its expansion and, following that, its negative consequences. In this scientific research, efforts were made to reduce the consequences of this phenomenon. The results of this study showed that although the phenomenon of compulsive purchase from TV is negative, but with proper management, useful results can be obtained from it.

For investors, predicting stock market changes has always been attractive and challenging because it helps them accurately identify profits and reduce potential risks. Deep learning-based models, as a subset of machine learning, receive attention in the field of price prediction through the improvement of traditional neural network models. In this paper, we propose a model for predicting stock prices of Tehran Stock Exchange companies using a long-short-term memory (LSTM) deep neural network. The model consists of two LSTM layers, one Dense layer, and two DropOut layers. In this study, using our studies and evaluations, the adjusted stock price with 12 technical index variables was taken as an input for the model. In assessing the model's predictive outcomes, we considered RMSE, MAE, and MAPE as criteria. According to the results, integrating technical indicators increases the model's accuracy in predicting the stock price, with the LSTM model outperforming the RNN model in this task.

Purpose: Despite the widespread diffusion and interest aroused by content marketing, little attention has been paid until this moment to building a framework that presents the main currents and studies of the field. Hence, the main aim of this study was to cover this gap by analyzing bibliographic information as complementary sources and enable a wider understanding and grasp of the content marketing field.Method: For this purpose, a bibliometric study of the publications indexed in Web of Science (WoS) between 1985-2022 was conducted. The search process used in this review was informed by PRISMA guidelines. During the search process, a set of 371 documents (research  and review articles) were obtained. Also, the bibliometrix R-package and VOSviewer software were used for quantitative analysis and visualizing bibliometric networks.Findings: The descriptive statistics showed that content marketing studies have rapidly grown since 2011. The US and Spain are the countries with the most publications of the field. The most prominent journal concerning content marketing research is Brand Journalism (with 11 articles), and the most prolific author is Bull A (with 11 articles).The results of the thematic analysis showed that ‘digital marketing’ and ‘brand storytelling’ are emerging themes and have replaced ‘content marketing’. The co-word analysis of author’s keywords defines 8 clusters: 1) platforms and techniques 2) content marketing concepts, 3) influencer marketing and advertising, 4) digital and social media marketing, 5) brand management and brand storytelling, 6) brand journalism, 7) private and native media, and 8) corporate and public communication.Conclusion: Simultaneously with the development of content creation platforms, these platforms have been welcomed in the field of content marketing. Content preparation has undergone changes in recent years. The style of information content based on news and specialized knowledge has shifted its focus to storytelling and narrative messages from the brand. This paper introduces the main areas of interest and possible gaps. It also contributes to the body of knowledge by providing a comprehensive overview of content marketing literature.

Purpose: This research aims to identify the factors affecting social network marketing based on gaining a competitive advantage and designing a model related to the communication between them.Method: This research is applied in its purpose, and conducted using a mixed approach (qualitative and quantitative). In the qualitative phase, first by reviewing the literature in the field of social network marketing and using the meta-combination method, the factors affecting it were identified and divided into three categories. subsequently, the relationships between these factors were determined and analytically analyzed using the interpretative structural modeling approach. In the modeling section, a questionnaire was distributed among experts selected through snowball sampling.Findings: The results of this research led to the classification of factors affecting social network marketing based on gaining competitive advantage and designing a model of these factors. In this model, the categories of organizational and structural resources (responsiveness to customers, service quality), marketing (attracting more customers, efficiency), and competitive advantage based on social media (branding, profitability), indicate the factors and the way of communication and interaction of these factors.Conclusion: Finally, the interpretation of the model, analysis of its levels, and the relationships among dimensions were discussed and solutions were provided in this regard.

The eXtensible Business Reporting Language (XBRL) functions as an independent, open platform that facilitates efficient information transmission over the Internet, improving business information utilization. Despite its widespread adoption and numerous benefits, unresolved assurance issues undermine its effectiveness, revealing a significant research gap. This study explores the complex landscape of XBRL data assurance challenges within the Malaysian Business Reporting System (MBRS). Utilizing a qualitative case study methodology, the research highlights key challenges in XBRL data assurance and presents strategic, innovative solutions. Through semi-structured interviews and document analysis, insights from diverse stakeholders are captured, revealing the development of artificial intelligence-enhanced audit software aimed at improving the quality of XBRL filings in Malaysia. Despite its potential, awareness of this advanced software among preparers remains disappointingly low. This research serves as a valuable resource for practitioners and researchers, offering an in-depth analysis of XBRL data assurance challenges and pioneering solutions, thereby making a significant contribution to this critical field.

With the advent of user-generated text information on the Internet, text sentiment analysis plays an essential role in online business transactions. The expression of feelings and opinions depends on the domains, which have different distributions. In addition, each of these domains or so-called product groups has its vocabulary and peculiarities that make analysis difficult. Therefore, different methods and approaches have been developed in this area. However, most of the analysis involved a single-domain and few studies on cross-domain mood classification using deep neural networks have been performed. The aim of this study was therefore to examine the accuracy and transferability of deep learning frameworks for the cross-domain sentiment analysis of customer ratings for different product groups as well as the cross-domain sentiment classification in five categories “very positive”, “positive”, “neutral”, “negative” and “very negative”. Labels were extracted and weighted using the Long Short-Term Memory (LSTM) Recurrent Neural Network. In this study, the RNN LSTM network was used to implement a deep transfer learning framework because of its significant results in sentiment analysis. In addition, two different methods of text representation, BOW and CBOW were used. Based on the results, using deep learning models and transferring weights from the source domain to the target domain can be effective in cross-domain sentiment analysis.

Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.

The unpredictability of the stock market makes it a serious area of study and analysis. With the help of the accumulated information available in the current digital age and the power of high-performance computing machines, there is a great focus on using these capabilities to design algorithms that can learn stock market trends and successfully predict stock prices. The main goal is to create an intelligent system that provides these features for predicting short-term stock price trends to facilitate the investment decision process. To increase the accuracy and productivity of these systems and facilitate the routine of using common-sense knowledge in machine learning systems, developing or enriching knowledge bases and ontology for market modeling will be one of the effective measures in this field. In this research, an attempt has been made to strengthen and enrich the basic ontology created by the authors by using other global ontologies related to the subject of the stock market, and parts of the target space that were not addressed have been added to the ontology. By combining reference ontologies, a level of standardization is also created for the ontology and stability in the representation of concepts and relationships is ensured. In the next step, it has been tried to test the impact of the concepts and relations of the ontology in predicting stock price movements. For this purpose, news in the field of economy is considered as input and a model is created that first filters the textual inputs related to the desired stock symbol and then observes their effect on the price changes of the related stock. After improving the performance and comprehensiveness of the ontology, the study conducted in this report presented a model to measure and prove the effect of the relationships in this ontology on price changes. In practice, according to human limitations and the tools used, this effect was observed and confirmed with a proper level of certainty by checking the economic news.

Purpose: The development of e-commerce and online shopping has given rise to emerging concepts of consumer behavior, including webrooming. Due to the novelty of the concept of webrooming in this study, an attempt has been made to provide a conceptual framework to explain this behavior and the factors affecting its formation.Method: In this regard, a field survey study was conducted by distributing questionnairy among a sample consisting of 384 gold and jewelry customers in Tehran. The questionnaires consisting of 9 dimensions and 38 items were distributed among the members of the statistical sample after ensuring reliability and validity. Data analysis along with partial least squares technique and Smart PLS software were used.Findings: According to the results, the benefits of online and offline channels have a significant impact on webrooming attitude; It was also discovered that attitude, perceived risk, anticipated regret, subjective norms and behavioral control have a significant impact on behavioral inclination and webrooming.Conclusion: The results of goodness of fit showed that the proposed model in this research has a good validity and fit. Given that webrooming has a negative impact on online sales; the results help online retailers mitigate this phenomenon by targeting webrooming antecedents.