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Purpose: The purpose of the research was to understand the cross media electronic marketing process. In fact, with the emergence of virtual organizations, social network media marketing has become a basic need for the possibility of movement and continued developmentMethod: this research conducted using Grounded theory analysis. Snowball or targeted sampling is utilized to gather the required data. The qualitative part of this study was conducted based on the viewpoints of 24 university professors, marketing activists, experts, elites, managers, and employees of insurance companies. Collected interviews were analyzed in MAXQDA software. To achieve this goal, the collected interviews are evaluated using different coding methods as the main process. The interviews were entered into the MAXQDA software. Coding in data theory methodology is a process of conceptual abstraction by assigning general concepts (codes) to individual occurrences in the data.Findings: Based on the analysis, 104 primary codes, 18 core categories have been identified in 6 categories of the foundation's data. With the emergence of electronic commerce, organizations are changing their business. The proposed model shows that the digital era has changed the expectations of customers from communication with brands. Before social networks, mass media communication channels such as television and radio to brand managers. It allowed them to communicate with consumersConclusion: In the present research, based on the participants' viewpoints, the categories of management factors challenge, financial potential, communication potential, customer characteristics and welfare and comfort, finally, marketing techniques have been identified and it has been related to another broader category called causal conditions. Information technology and targeted marketing were identified as background conditions. Environmental factors and psychological factors of the clients were identified as intervening conditions. Research strategies include business improvement, knowledge and education, and finally innovation and creativity. Outcomes include customer loyalty, sustainability in customer relationships with the product and social media, beneficial word-of-mouth and viral advertising, customer satisfaction, and purchase intent.

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.

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.

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.

In an era where customer expectations are rapidly evolving, enhancing the efficiency of hotel reception services in Iran is crucial for the growth of the hospitality sector. Recent research highlights the importance of digital transformation in improving service delivery and operational efficiency in the hospitality industry. These studies indicate that technological advancements can significantly streamline operational processes, improve customer satisfaction, and foster a competitive advantage in the hospitality industry. This research presents a technological innovation model aimed at modernizing reception desk services, addressing the pressing need for improvement in this area. Using an interpretive paradigm and an inductive approach, we conducted a qualitative study that incorporated a systematic review. Subsequently, the structures and components were extracted from the studies through qualitative coding. Our findings, derived from a review of 54 studies, revealed 295 open codes distilled into 15 constructs and four main components. This study highlights the significant impact of technological innovation on reception services, emphasizing the roles of ease of use and perceived usefulness in the technology adoption process. These insights provide essential guidelines for advancing reception desk technologies within the Iranian hotel industry, ultimately contributing to enhanced service quality.

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.

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 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.

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.

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.

Purpose: The purpose of this research was to provide a model for choosing the best content for the activity of service guilds.Method: In inferential statistics, the K-S test is used for the normality of research hypotheses. For this purpose, Pearson's correlation coefficient and linear regression tests have been used through SPSS 21 software, and the best content generated using genetic algorithms and forbidden search were introduced.Findings: Analysis of research and implementation results with two collective intelligence algorithms shows that Instagram has a positive and significant effect on all four dimensions and thus leads to the success of the service classes that have used Instagram.Conclusion: In this article, a combination model of genetic algorithm and forbidden search algorithm was chosen for users so that the best content, which of course does not contain malicious ads and cookies, etc., is introduced for the continuation of the service industry.

The adoption of E-learning in academic environments, particularly in Iran, has accelerated over the past decades. However, the lack of established guidelines for instructional design seems to have hindered the creation of effective online educational environments. Therefore, this study aimed to conduct a comprehensive qualitative research synthesis (QRS) to identify the criteria for an optimal online learning environment for Iranian university students. The study followed Major and Savin-Baden's three-phase QRS model and included 12 studies with 252 participants and 42 researchers. The synthesis integrated findings from multiple studies to gain insights into experts’ opinions and students’ perceptions, preferences, and experiences within online learning environments. The research identified four overarching themes: targeted learning, effective teaching management, socio-affective engagement, and learner empowerment. Targeted learning focuses on problem-oriented and need-oriented teaching; effective teaching management emphasizes balanced content and time management, clear objectives, and diverse presentation and evaluation methods; socio-affective engagement involves interactive feedback, social presence, and emotional communication; learner empowerment stresses autonomy, agency and active learning, including experiential and discovery learning. Applying these findings seems to offer a genuine contribution, leading to the development of culturally relevant and high-quality E-learning experiences and addressing the challenges posed by the nascent E-learning system in Iran.

Cyberbullying involves using hurtful or offensive language that goes against basic rules of respect and politeness. It harms the online environment and can negatively affect people by causing harassment, discrimination, or emotional pain. To combat this, it is crucial to develop automated methods for detecting and preventing the dissemination of such content. Deep learning, a branch of artificial intelligence, leverages neural networks to learn from data and perform complex tasks, effectively capturing semantic and grammatical nuances to differentiate between abusive and non-abusive language. This survey paper reviews current techniques and advancements in deep learning-based approaches for detecting cyberbullying content on online platforms, aiming to provide a comprehensive understanding of existing methodologies and identify potential avenues for future research to mitigate the spread and impact of such behaviors on the internet.

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.