درخت حوزه‌های تخصصی

The increasing progress of technology has affected all aspects of human life. One of the new technologies for which various applications have been created today is virtual reality technology. Virtual reality seeks to create a sense of reality in an unreal environment. The aim of this research is the effect of virtual reality technology on the creation of customers' attitudes and understanding of property and service providers in the real estate consultant department in Shiraz. The statistical population includes all real estate clients and those who refer to the real estate sector in Shiraz, and since the population is considered, it is unlimited; The number of statistical sample is 384 people. In this research, the standard questionnair of Players and Ponsin (2020) were used to collect research data. Structural equation modeling approach and PLS3 software were used for data analysis. The results showed that virtual reality technology has a significant effect on creating the attitude and understanding of customers towards the property and the service provider in the real estate consultant sector. The combination of virtual reality technology with augmented reality is one of the measures that can significantly improve the attractiveness and efficiency of using virtual reality in the field of real estate.

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 financial anomalies, which have brought about challenges for the nature of efficient-market hypothesis, provide ample experimental evidence on the deviation of market from rationality. According to the efficient-market hy-pothesis, investors avail themselves of all the information available in the market, and then attempt a thorough and painstaking interpretation of it. The empirical evidence, however, reveals that most investors fail to imple-ment rigorous information processing, thereby rendering to a number of behavioral theories concerning market inefficiency and biased information processing. The current research sets out to identify and scrutinize the im-pact of the CEO’s perceptual biases in judgment and economic decision-making on the reporting quality of the firms listed on the Tehran Stock Ex-change. The Purpose of this study CEO Overconfidence and Overinvestment by Role of Economic moderators Tehran Stock Exchange (TSE). One hun-dred five companies were selected as the sample size by systematic removal sampling reviewed in 2012-2019. Due to its dichotomous dependent variable, this study uses probit regression to test the research hypotheses. The results indicated the significant positive effect of CEO overconfidence on overin-vestment. It was also noted that Exchange Rate strengthens the positive effect of CEO overconfidence on overinvestment. Based on these results, the CEO's decisions as a decision-maker in charge of any company, especially under inflationary conditions, can play a substantial role in future corporate investment levels. The findings reveal that the indices of the CEO’s percep-tual biases in judgment and economic decision-making exert a significantly negative effect on financial reporting reliability and competitiveness.

Due to the rapid advancements in computer technology, researchers are attracted to solving challenging problems in many different fields. The price of rainbow options is an interesting problem in financial fields and risk management. When there is no closed-form solution to some options, numerical methods must be used. Choosing a suitable numerical method involves the most appropriate combination of criteria for speed, accuracy, simplicity and generality. Monte Carlo simulation methods and traditional numerical methods have expensive repetitive computations and unrealistic assumptions on the model. Deep learning provides an effective and efficient method for options pricing. In this paper, the closed-form formula or Monte-Carlo simulation are used to generate data in European and Asian rainbow option prices for the deep learning model. The results confirm that the deep learning model can price the rainbow options more accurately with less computation time than Monte-Carlo simulation.