درخت حوزه‌های تخصصی

This article deals with developing a model of digital marketing with a focus on customer experience and with the aim of optimizing the customer management process, increasing the quality and length of the customer journey (increasing loyalty), and improving the consequences of the marketing process in companies, and seeks to answer this question. What is the digital marketing model with the customer experience management approach and what components does it include? The research method is mixed (qualitative-quantitative) and since a model for this issue is not available in past research, the first part of the research explores model extraction through qualitative methods (here, foundation data analysis) and to determine Relationships within the model quantitatively explain the model using quantitative methods (here structural equations). The method of collecting information in the qualitative part, in addition to conducting library studies, in the form of in-depth interviews with experts in the fields of information technology, marketing, and commerce, information technology policy, and actually business managers and stores that provide services and products through channels and digital tools in this section, the statistical sample limit was carried out by the snowball method (saturation to the end of the problem) and the quantitative part of the research and explanation of the model was through data collection with questionnaire tools and closed interviews from the customers of several digital businesses, which was done with the method 385 people were randomly selected and available using Cochran's formula. In the end, the main conditions, background conditions, intervening conditions, phenomena, strategies, and consequences affecting the digital marketing model with the customer experience management approach were extracted and the relationships between them were determined.

The aim of present study is to identify the characteristics of human resource management with a strategic thinking approach in the Organization for the Collection and Sale of Proprietary Property in Iran. In terms of aim, the present study is an exploratory study based on semi-structured interviews, and in terms of results, it is developmental. The statistical population of the study included executive experts who were fully familiar with the Organization for the Collection and Sale of Proprietary Property. The sampling method in the qualitative section was snowball sampling. A total number of 15 managers and officials of the Organization for the Collection and Sale of Proprietary Property of Iran, including members of the Board of Directors, consultants and managers, and deputies and heads of departments was selected in this section. The first step involved identifying human resource management indicators with a strategic thinking approach through interviews with experts and qualitative data analysis (thematic analysis). Also, to confirm the extracted themes and check the reliability of the interviews and reach a consensus on the extracted themes, a questionnaire was designed based on the findings of the thematic analysis and completed by experts and fuzzy Delphi analysis was performed. Based on results of research regarding the indicators for human resource management with a strategic thinking approach in the Organization for the Collection and Sale of Proprietary Property, five main indicators, including situational factors, stakeholders’ group, and selection of human resource policy, achievements and long-term results were identified.

The asymmetry of information between managers and shareholders significantly affects the company's investment decisions and exacerbates the problems of agency. It seems that these problems in developing markets such as Iran are more than developed markets. This research is an exploratory study, and it aims at reviewing the theory of agency in Iran. In the present study, based on the research paradigm, first, data acquisition strategy is selected based on a qualitative approach, and then qualitative study and analysis, quantitative approach, and factor analysis approach have been used to confirm the validity of the extracted model. Statistical analysis of research data shows that among the hypotheses of agency theory, risk aversion, supervision, and reward are revised dimensions in Iran like examples of developing countries.The results confirm the existence of assumptions; 1. Conflict of interests of manager and owner, 2. Information asymmetry, 3. Reverse choice, 4. Ethical risk, 5. Maximizing the expected utility, 6. Forcing and 7. Failure to confirm the assumption is the ability to predict the future and receive information. due to the existence of information asymmetry, higher investment risks of minority shareholders and transparency problems, the lack of ability to predict the future and receive information is necessarily confirmed.

Nowadays, many companies cannot see the digital investment that plays a main role in the IR 4.0. Therefore, this study is investigating the study of investment as plays a critical role in an analytical activity to assess the benefits and costs of an investment and can be used as an investment justification. Traditional investment appraisal uses a financial approach where the benefits and costs are quantified in a certain amount of value for money and then compared in value. Moreover, this study is revealed the fruitful outcomes because revealed the investment valuation method with NPV (Net Present Value) and ROV (Real Option Valuation). ROV is an alternative to financial valuation. Seeding from the same philosophy as Financial Option, ROV has advantages in handling the flexibility, risk, and volatility that may occur from an investment. Thus, ROV is considered more able to appreciate an investment that has these characteristics. Investment appraisal with ROV is better able to appreciate investment than traditional financial methods, as shown by ROV's NPV results in the case of marketing with Loyalty points through email communication as a digital investment that are greater than ordinary NPV. This is because ROV can appreciate flexibility in investments that have choices of investment plans in the future

What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions

Sports tourism is one of the most attractive types of tourism that occurs in different regions and countries. This  tourism type is effective in economic fields and extremely significant in cultural and sportsmanship contexts.  To this end, the current research aimed to identify and explain the effective components of sport tourism in the  sublimity of culture and sportsmanship, conducted through a mixed method. This research approach was  sequential exploratory in two qualitative-quantitative phases, in which “thematic analysis” and “survey”  methods were used, respectively. The first phase’s statistical population included experts in sport tourism, sport  management, culture management, and sportsmanship, with 14 individuals participating in semi-structured  interviews using a purposive sampling method. After determining the effective actors in this field, 32 experts  responded to questionnaires using a systematic sampling method in the study’s second phase. According to the  research findings of the first phase, the themes extracted from the semi-structured interviews comprised 39  fundamental themes, 15 organizing themes, and four global themes. Per the second phase of the study’s  findings, all themes were approved by the experts, all determinants of the role of sport tourism in the sublimity  of sports culture and sportsmanship. As a result, sports tourism, while creating macroeconomic benefits for  residents and visitors, significantly contributes to the sublimity of sports culture and sportsmanship, where this  category can strengthen the value and ethical attitudes in addition to creating recreational experiences

In this article, we will examine the financial issues related to multi-period routing and positioning and the related costs, and we will examine the related limitations. These decisions are made about location allocation, inventory and routing in a three-tier supply chain, including suppliers, warehouses and customers. We are looking for new ways to make location and routing decisions simultaneously and efficiently. In order to maximize the search space and achieve optimal results, exploratory and meta-heuristic methods have been used. The meta-heuristic technique is usually used to increase the performance of the hybrid technique. Therefore, this paper provides an overview of meta-heuristic methods and their combination to solve problems. It also examines the advantages and disadvantages of the proposed methods to solve these problems in order to provide more efficient methods.

This study examines empirically the effect of equity overvaluation on future stock price crash risk in companies with greater information asymmetry. Using the information asymmetry and crash risk indicators, the question that whether overvalued firms are more prone to future crash risk is investigated. Accordingly, the first purpose of this study is to investigate the relation between overvaluation and the future stock price crash risk. With the increase of information asymmetry between a firm and the market, managers have more abilities and opportunities to withhold bad news and accelerating the release of good news. As a result, it is expected that the information asymmetry between managers and investors increases future stock price crash risk. In so doing, we identify one main hypothesis and three subsidiary ones and the data of 111 listed companies of Tehran Stock Exchange for the period between 2009 and 2017 were analyzed and A panel data approach has been used to test of research hypotheses. According to the results, there is a positive and significant relationship between the overvaluation and the future stock price crash risk of companies which are operating in monopolistic markets and this relation intensified by the information asymmetry.

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.