درخت حوزه‌های تخصصی

This study is aimed to explore the structural relationships among the factors affecting the employees' voice. To do so, by reviewing the literature a series of factors influencing the occurrence of organizational voice was identified and then the opinions of 15 senior and middle managers and academic professors about the relationships between these factors were examined. Finally, data were analyzed by utilizing the Interpretive Structural Modeling (ISM) and Fuzzy MICMAC methods. According to findings, organizational culture has an underlying role in organizational voice. In fact, it can be said that existence of an ideal organizational culture can be reflected in the improvement of employees’ knowledge, understanding about the organization, awareness and their experiences. These factors also increase the psychological safety and eventually, their self-confidence. Moreover, employees’ self-confidence also influences the willingness of organization for accepting and valuing their voice, their change commitment, job satisfaction and openness to experience. Finally, in an interaction to each other, these factors increase the employees’ courage to express their ideas. As a general result, this research showed that the appearance of organizational voice is a sign of the existence of an organization's desirable culture.

The aim of the current research is to identify the futures of marketing in the banking industry with a focus on blockchain technology. The current research is applied in terms of direction and has a mixed methodology due to the use of qualitative and quantitative methods together. The theoretical population of the research was experts in banking marketing and digital financial technologies, especially blockchain, and the sampling method was done in a judgmental method. The sample size in this study was equal to 15 people. For data analysis, meta synthesis methods, Binominal's statistical test, developed Copras and root definitions tool were used. The tools of data collection in this research were interviews and questionnaires (expert evaluation and priority evaluation questionnaires). 47 drivers were extracted from meta synthesis and these drivers were classified into nine main drivers. The drivers of the research were screened in two stages using theory-based inference screening and Binominal's test, and 12 drivers were considered for prioritization using the Copras technique. The remaining drivers were evaluated using the Copras technique and three criteria of importance, degree of certainty and experts' expertise. The findings showed that the drivers of marketing researchers' interest in digital financial technologies and blockchain and the development of decentralized banking had the highest priority and were selected for scenario planning. Based on these two drivers, four scenarios of crypto bank, conservative banking, pioneer banking and traditional banking were developed. The research proposals were proposed based on the important drivers and the desired scenario (crypto bank).

The purpose of a financial manager in dividend policy is to maximize the wealth of its owners while providing adequate funds for the company. In this research, the aim is to determine the relationship between information asymmetry, dividend policies and ownership structure. After designing information asymmetry evaluation indicators, the transaction information has been collected from the Stock Exchange in the five year period of 2011-2015. A statistical sample of 155 companies was selected using a systematic elimination method, which was a total of 775 years-firm. In this research, linear regression and correlation were used to analyze the hypotheses of the research. What can be said in the summing-up and conclusion of the general test of research hypotheses is that the company with a higher information asymmetry is less likely to pay dividends, as well as government-affiliated companies with higher information asymmetry pay more dividends compared to non-governmental corporations. Finally, the results indicated that structural reform in order to increase the transparency of information leads to a positive moderating effect on the relationship between information asymmetry and dividend policies

Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has been used to improve return forecasting and then compare the results with historical average methods as bench mark model and use Diebold and Mariano’s and West’s statistic (DMW) for statistical evaluation. Results indicate that the applied DP model has higher accuracy compared to historical average model. It also indicates that out of sample prediction improvement does not always depend on high input variables numbers. On the other hand when using gold price as input variables, it is possible to improve this forecasting capability. Result also indicate that gold price has better accuracy than Goyal's variable to predicting out of sample return.