درخت حوزه‌های تخصصی

The present study aimed to rank banks in terms of board of directors report and notifying the users of reports. In addition, we evaluated factors affecting risk disclosure from the perspective of corporate governance. Moreover, we assessed risk disclosure based on linguistic analysis of board report text and capital adequacy ratio. Words were applied as measurement units to measure risk disclosure. The advantage of this technique is the unique analysis of words. According to the theoretical foundations presented in the present study, we first identified risk disclosure words in reports provided to financial information users and divided them into two categories of positive and negative risk disclosure words. Another variable selected for risk disclosure was capital adequacy ratio. Effective variables in corporate governance system in banks included the board independence, duality of CEO duties, and major shareholders as input variables in data envelopment analysis (DEA) model. On the other hand, the BCC model of DEA was selected as output-based nature. The statistical population included all banks listed in Tehran Stock Exchange. In total, 20 year-bank units listed during 2016-2017 were assessed. In the end, seven year-bank units were considered efficient while the rest were inefficient. Moreover, we estimated the amount of shortage in outputs to reach inefficient banks to the desired level of efficiency.

Opportunistic networks in the Internet of Things (IoT) scenario, also known as OppIoT, espouse IoT devices interactions opportunistically in order to improve connectivity, the lifetime of the network, and network reliability. An increase in opportunistic utilization is fostered by IoT applications to find communication opportunities whenever possible to route and deliver data efficiently. In this opportunistic scenario, devising an efficient path for data delivery is a challenging work due to uncertainty in the connection between the nodes and the selection of intermediate forwarder nodes for data delivery towards the destination. Considering the scenario of uncertainty in device location and exploiting IoT devices opportunistically, this paper propounds a routing algorithm for OppIoT called Hybrid Multi-Copy Routing Algorithm (HMCRA). The proposed algorithm finds potential forwarder nodes by using fuzzy logic wherein residual energy, distance, and speed of the nodes are considered as input values while preparing fuzzy rules. Genetic Algorithm (GA) is considered along with fuzzy logic to select an efficient path for data delivery. In GA, the delay is taken as the fitness function to select a reliable path for data delivery. Simulation results of the proposed algorithm perform well in contrast with relative existing routing algorithms with respect to latency, overhead ratio, delivery probability, and hop count. The work uniqueness lies in the selection of potential nodes and finding path having less hop count in an opportunistic IoT network scenario.

Personality is one of the most important study structures in organizational behavior that can play an important role in predicting human behavior. To describe the differences in personality, emotional, and social behavior, researchers have proposed the theory of five personality factors, known as the Big Five. One of the decisions investors make is to allocate wealth to financial and non-financial assets. The importance of this decision is especially evident in financial assets due to their nature and the need for more knowledge and expertise in this field because the mistakes of investors in this regard can create many risks and challenges for them. The main purpose of this study is to explain the effect of personality traits and demographic features on the risk-taking behavior of investors in the Tehran Stock Exchange. The present research is applied research in terms of goals and descriptive-survey research from the method view. The statistical sample of this research includes 358 investors of the Tehran Stock Exchange during 2019 and 2020. Research data were collected based on Sivarjan's (2018) questionnaire. The results of data analysis using the structural equation modeling method indicate that personality traits including neuroticism, extraversion, openness, agreeableness, and conscientiousness have a positive and significant effect on risk-taking behavior. Still, demographic characteristics including gender, age, marital status, and education do not have a significant effect on risk-taking behavior.