The main objective of the present study is to investigate the risk factors influencing on construction firm’s project success in Pakistan. The study uses the partial least square structural equation modelling technique to analyse the data collected from 61 construction firms. The study finds positive impacts of design risk, financial risk, technical risk and labour risk on the success of the project. Alternatively, the current study finds no relationship between external risk factors and the success of the project. Furthermore, the current study suggests government and construction firms to consider the risk factors during the decision making for project implementation.

This study aims to analyze and examine the influence of internal auditor certification, risk assessment, and the role of senior management on the effectiveness of cybersecurity for internal auditors who have experience in cybersecurity and information technology. This research method is a quantitative method, data analysis uses structural equation modeling (SEM) with SmartPLS 3.0 software tools. The population of this study is internal auditors who have experience in cybersecurity and information technology. The sample for this study was 480 respondents who were determined by the snowball sampling method. The research data was obtained from an online questionnaire which was distributed via social media. The questionnaire was designed using a Likert scale of 1 to 5. The stages of data analysis were validity test, reliability test and significance test. The results of this study indicate that internal auditor certification has a positive effect on cybersecurity effectiveness, risk assessment has a positive effect on cybersecurity effectiveness, and the role of senior management has a positive effect on cybersecurity effectiveness.

This paper investigates the role of corporate disclosure policy on risk assessment and market value on selected firms from Tehran Stock Exchange over the period 2004-2012. The study considers four risk namely; business risk, cash flow risk, financial risk and systemic risk. Using regression analysis with panel data under fixed effect, the study shows that there was a meaningful relationship between two risks, business risk and systematic risk, and market value before and after corporate disclosure. In other words, after corporate disclosure, the relationship between risk and market value becomes stronger.

The numerous studies on comparing and ranking fuzzy numbers clear that this task is still young. However, the observation that many papers do not hesitate to use some incorrect ranking methods in risk analysis problems, encouraged the author to point out the shortcoming of some of these methods. In this note, we review briefly the methods for ranking fuzzy number in risk analysis.

The purpose of this paper is to examine the existing risks for the offshore project and risk weighting using the fuzzy best worst method (FBWM). In offshore oil projects, we face six major risks. Each of these risks is divided into smaller risks leaving us to have a total of 34 risks. Some of these risks are internal and some are external risks. In this method, first, the experts of this field determined the best and the worst type of risk. Then, using the experts’ opinions, the study compared the remaining risks with the two selected risks and the other weights are determined. In our survey, “Technical Risk and Project Execution” is the most important risk factor followed by “Political Risk and Sanctions”, “Market risk”, “Manage-ment risk”, “Financial risk and currency fluctuations” and “Environmental risk”.

This paper presents a logistic regression model to measure risk management of receivable accounts on some selected firms from drug industry listed on Tehran Stock Exchange. The proposed study of this paper considers the effects of different variables such as current ratio, quick ratio, working capital on total assets and cash flow on economic value added. We gather the necessary information of 29 firms over the period 2006-2011. The results of our survey indicate that the proposed model of this paper is capable of forecasting high profit firms with a probability of 87.5%, 58.62% is the likelihood of predicting less profitable firms and on average, the firm could forecast profitable firms for 75.54%, successfully.

Mining is one of the most important sectors in most countries. It produces raw material for other sectors such as industry, agriculture, etc. Therefore, governments always seek the solutions to prevent or at least reduce the risk of mining industry to minimize the waste of time and resource. One of the most popular risk in mining industry that should be clearly assessed is supply chain. There is a variety of methods to evaluate and classify risks. Fuzzy set is one of the most appropriate methods to categorize and evaluate risks, because this method is able to take into account the uncertainty involved in the process of risk assessment. In this article, fuzzy inference system is applied to evaluate and assess the supply chain risk of the Iranian mining industry. This research shows that the proposed model had a high accuracy and efficiency for assessing the risk of mining industry.

Electricity distribution systems are considered as the most critical sectors in countries because of the essentiality of power supplement security, socioeconomic security, and way of life. According to the central role of electricity distribution systems, risk analysis helps decision maker determine the most serious risk items to allocate the optimal amount of resources and time. Probability-impact (PI) matrix is one of the most popular methods for assessment of the risks involved in the system. However, the traditional PI matrix is criticized for its inability to take into account the inherent uncertainty imposed by real-world systems. On the other hand, fuzzy sets are capable of handling the uncertainty. Thus, in this paper, fuzzy risk assessment model is developed in order to assess risk and management for electricity distribution system asset protection. Finally, a comparison analysis is conducted to show the effectiveness and the capability of the new risk assessment model.

Cultural heritage and properties in collections are the most important organizational resources of museums, which can be deteriorated under improper environmental conditions. Identifying the climate deteriorating agents, and assessing and prioritizing their risk levels are among the most recent issues studied by museum conservators and researchers. Creating risk management programs leads to enhancing service life of cultural properties. Temperature and relative humidity are two important factors related directly to objects permanence; Thus, using methods which combine and quantify the conservation effects of temperature and relative humidity, could yield to useful and reliable insights for proposition of effective preventive conservation solutions. This research tries to explain the role and importance of risk management programs in the museums -as one of the main pillars of the country & apos; s cultural heritage; therefore, by using a multidisciplinary approach and existing experiments, proposes a method to examine and assess risks of temperature and relative humidity on museum objects. Such quantitative analysis could help estimate objects & apos; service life and provide solutions in order to enhance it. In order to examine the proposed research method, temperature and relative humidity data were gathered from textile storage room of the Cultural Institute of Bonyad Museums in a period of 18 months.