درخت حوزه‌های تخصصی

The human resource management works closely with all the employees in the organization. An HRM must allow the employees to make constructive criticism when there is a need for it. The duty of organizing the company towards achieving their set goals lies in the hands of the HRM. A Human Resource management process can best be distinct as a tool which is utilized to collect, organize, present, keep and share applied information about the human resource of an organization. To this end, this research present a Throughput model framework that describes individuals' decision-making processes in an algorithmic HRM context. The model depicts how perceptions, judgments, and the use of information affect strategy selection, identifying how diverse strategies may be supported by the employment of certain decision-making algorithmic pathways. In focusing on concerns relating to the impact and acceptance of artificial intelligence (AI) integration in HRM, this research draws insights from multidisciplinary theoretical lenses, such as Al-augmented and HRM assimilation processes, AI-mediated social exchange, and the judgment and choice literature. Results highlight the use of algorithmic ethical positions in the adoption of AI for better HRM outcomes in terms of intelligibility and accountability of AI-generated HRM decision-making, which is often underexplored in existing research.

The main drivers of value creation in a ‘brand community’ are social networking, community engagement, impression management, and brand use. Marketers are therefore interested in determining which factors affect the value creation practices. This study examines the impact of the Interactivity of Electronic Word of Mouth (EWOM) systems on value creation practices in a brand community, which in turn influences the loyalty of the customers. In this regard, a conceptual model was developed and tested by the researchers of the current study. The results indicate that perceptions of the users regarding the interactivity of EWOM systems, highly impact only three of the four value creation practices including community engagement practices, impression management practices, and brand use practices. Furthermore, the researchers found that collective value creation practices could significantly and directly enhance brand loyalty. Several theoretical contributions and managerial implications were also discussed

INTRODUCTION: Considering the significant contribution of public education to the reduction of damages incurred by disasters and crises in the country, the identification of all the components and dimensions of public education can be of great help to policymakers in this field in order to adopt a correct policy and decision. The current research aimed to investigate public education in natural disasters with the meta-synthesis method based on the seven-step model of Sandelowski and Barroso (2007). METHODS: The current qualitative research was conducted based on a meta-synthesis approach. The statistical population of the research included articles related to the research from 2000 until now. A number of 175 articles were retrieved, and after examining the title of the research, 105 articles were removed, and 70 papers were reviewed, out of which 36 papers were removed, and 34 articles entered the final stage for full analysis. Articles were searched in databases, such as Scopus, Emerald, Science Direct, Springer, Google Scholar, and Scientific Information Database (SID). FINDINGSThe selected sources were analyzed using Maxqda software, and the research findings were coded. In the open coding stage, 100 primary codes were extracted from 34 articles, and after extracting the primary codes, to categorize the concepts, axial coding was used, yielding 5 dimensions, 12 components, and their sub-components. CONCLUSIONAs evidenced by the obtained results, to reduce vulnerability, the responsible institutions can adopt appropriate measures that lead to learning and changes in the audience's behavior by using the presented model and considering the determined components and indicators in the process of public education. This model includes the stages of analysis, educational design, preparation and development, implementation, and evaluation.

The issue of asset pricing in the market is one of the most important and old issues in the financial world. Factor pricing models seek to be able to determine a significant relationship between return on assets based on the risk parameters of that asset. A wide range of factors can be found in the literature that can be an element for measuring the risk of an asset, but the big question is which of these models will work better. The factors studied in this research include factors that cover market risk, valuation risk, psychological (technical) market risk, profit quality risk, profitability, investment, etc. In this study, we have tried to Using machine learning techniques and optimization tools is a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. In this research, two models have been developed. In the first model, using the feature extraction technique and optimization of models based on neural network, a non-linear and adaptable model has been developed for each asset. In the second approach, a portfolio of improved neural network-based models is used in the first stage, which can be used to minimize the risk of model error and achieve a model that is resistant to different market conditions. Finally, it can be seen that the development of these models can significantly improve the risk of error and average error of the model compared to traditional CAPM approaches and the Fama and French three-factor model.

The occurrence of unexpected phenomena in recent decades in financial markets around the world,led to the development of theories,beyond the defined principles and criteria of classic finance.These theories are based on financial psychology and they are explaining the role of psychology in financial sciences as an influential factor and became irreplaceable in financial markets and investor decisions.One of the important theories in this field is the bounded-rationality-theory,which can explain the behavior of decision-makers about financial and economical issues based on defined theoretical frameworks and assumptions.This study developed the stock pricing model by comparing the predicted stock price based on the bounded-rationality-theory and the real stock price through collecting the data of 122 companies listed on the Tehran Stock Exchange during the period 2011to 2019.The results of this study show the effectiveness of the bounded-rationality-theory based on the separation of stock return components and measuring the irrationality coefficient and emotional reactions of investors' decisions in stock prices.Accordingly,the limitations of investors 'ability to process information seem to affect the level of use of reasoning and rationality in decision-making and the effect of bounded-rationality through the irrationality and limited attention on stock pricing.Therefore,it is expected that knowledge about the process of bounded-rationality based on the rational-bounded of investors and the behavioral biases resulting from the irrational part of their thinking,will provide a good explanation for the process of changes in financial markets.This can provide both profit opportunities and costs in investment management so that it can be used in modeling,analysis and investment strategies.

Designing trading systems with good returns is critical for capital market investors. Trading systems are often based on a combination of several tools to use their combined information. For the first time in Iran, the present study aimed to propose a pattern detection algorithm for a flag pattern based on Japanese candlestick charts and their arrangement. By recognizing the pattern and if the 4- and 10-day moving average is confirmed, a shopping position is developed, and the selling time is determined based on an optimized and dynamic process commensurate with price changes and the data scale. Our objective was to address the question of whether the returns resulting from this strategy have a more significant positive return compared to the purchase and maintenance strategy. The research sample included the daily information of 16 active companies of basic metals in Tehran Stock Exchange during 2007-2019, extracted from the database of Novin Rahavard software. Data analysis was performed in MATLAB software, and the obtained experimental evidence was described using t-test. According to the results, the research strategy had a higher performance in terms of returns and risks compared to the market.

Uncertainty and critical conditions have become one of the most important challenges in the world today. In order to achieve superiority in economic competition, the supply chain must be designed in such a way that it has the necessary resilience in critical situations. One of the things that can be considered in this regard is the role of social media in the resilience of the supply chain. Considering the impact that social media has on business processes and considering the rapid and significant advances in information and communication technology, the use of social media in risk situations can be very effective. This research examines the role of social media in the resilience of the digital supply chain with a hybrid approach. The results of this research include 118 open codes, 43 core codes, and 13 main categories. The 13 selected codes of this research are placed in the form of five general categories of causal conditions, contextual conditions, intervening conditions, strategies, and consequences. Causal conditions include 2 selective codes, 8 concepts, and 21 open codes. The contextual conditions include 3 selected codes, 10 concepts, and 25 open codes. Intervening conditions include 2 optional codes, 7 concepts, and 21 open codes. The strategies include 2 selected codes, 7 concepts, and 19 open codes. The results include 4 selected codes, 11 concepts, and 32 open codes.