Enterprise Risk Management and Risk Culture in Construction Public Listed Companies

Inadequate risk management and lack of risk culture can expose a company to unexpected risk events, which can negatively affect its performance. However, there are inconsistencies in suitable dimensions to measure the enterprise risk management (ERM) construct, as well as insufficient embedding strategies for risk culture. This study aims to identify the ERM practices and risk culture dimensions among the Malaysian construction public listed companies (PLCs). The roles of top management and chief risk officer/risk manager in influencing ERM and risk culture are also explored. A total of 46 annual reports and 10 interviews of industry practitioners were analysed using content analysis. The analysis of the annual reports found that risk policy and risk appetite/tolerance, monitoring key risk and accountability are the three dimensions of risk culture. In addition, based on the interviews, reward and recognition and internal relationships were identified as the two dimensions of risk. Top management and risk manager were found to be the primary drivers of ERM programme and risk culture in construction PLCs. The results of this study are used to formulate a survey instrument for the subsequent data collection to test the proposed theoretical model.

Enterprise Credit Risk Management Using Multicriteria Decision-Making

The purpose of this study is to reduce the rate of multicriteria decision-making (MCDA) errors in credit risk management and to weaken the influence of different attitudes of enterprise managers on the final decision when facing credit risk. First, several solutions that are suitable for present enterprise credit risk management are proposed according to the research of enterprise risk management in the world. Moreover, the criteria and matrix are established according to the general practice of the expert method. A decision-making method of enterprise credit risk management with trapezoidal fuzzy number as the criteria of credit risk management is proposed based on the prospect theory; then, the weight is calculated based on G1 weight calculation, G2 weight calculation method, and the method of maximizing deviation; finally, the prospect values of the alternatives calculated by each method are adopted to sort and compare the proposed solutions. Considering the difference of risk degree of managers in the face of credit risk management, the ranking results of enterprise credit risk management solutions based on three weight calculation methods are compared. The results show that as long as the quantitative value of the risk attitude of the enterprise credit risk manager meets a certain range, the final choice of credit risk management scheme ranking is consistent. This exploration provides a new research direction for enterprise credit risk management, which has reference significance.

Statistical Analysis of New Zealand Electricity Prices: A Risk Manager's Perspective

<p>This thesis considers the conventional SARIMA model and the EVT-GARCH model for forecasting electricity prices. However, we find that these models do not adequately capture the important characteristics of the electricity price data. A new model is developed, the EVT-SARIMA model, for forecasting electricity prices which is found to be the best at modelling the nature of the electricity prices. A time series of half-hourly electricity price data from the Hayward node in New Zealand is transformed into a daily average price series and using this resulting series, appropriate models are fitted for estimating and forecasting. The new EVT-SARIMA model is used to simulate 1000 time series of daily electricity prices, over a 90 day period, to consider strategies for managing the risk associated with price volatility. The effects of different financial instruments on the cumulative distribution functions of predicted revenue obtained using our model are considered. Results suggest that different contracts have different effects on the predicted revenue. However, all contracts have the effect of reducing variability in the predicted revenue values and thus, should be used by a risk manager to reduce the range of probable revenue values. The quantity traded and which contracts to use is dependent on the objectives of the risk manager.</p>

Statistical Analysis of New Zealand Electricity Prices: A Risk Manager's Perspective

<p>This thesis considers the conventional SARIMA model and the EVT-GARCH model for forecasting electricity prices. However, we find that these models do not adequately capture the important characteristics of the electricity price data. A new model is developed, the EVT-SARIMA model, for forecasting electricity prices which is found to be the best at modelling the nature of the electricity prices. A time series of half-hourly electricity price data from the Hayward node in New Zealand is transformed into a daily average price series and using this resulting series, appropriate models are fitted for estimating and forecasting. The new EVT-SARIMA model is used to simulate 1000 time series of daily electricity prices, over a 90 day period, to consider strategies for managing the risk associated with price volatility. The effects of different financial instruments on the cumulative distribution functions of predicted revenue obtained using our model are considered. Results suggest that different contracts have different effects on the predicted revenue. However, all contracts have the effect of reducing variability in the predicted revenue values and thus, should be used by a risk manager to reduce the range of probable revenue values. The quantity traded and which contracts to use is dependent on the objectives of the risk manager.</p>

Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 8, Number 6 Albert Henry Ntarmah, Jiangsu University, GhanaAndualem Ufo Baza, Wolaita Sodo University, EthiopiaMarco Muscettola, Independent Researcher-Credit Risk Manager, ItalyOmer Allagabo Omer Mustafa, Sudan Academy for Banking and Financial Sciences., SudanRajeev Rana, APB Govt. P.G. College, IndiaRomeo Victor Ionescu, Dunarea de Jos University, RomaniaSebastian Schich, Organisation for Economic Coopertaion and Development (OECD), FranceY. Saidi, M’sila University, Algeria Nikki GibbsEditorial AssistantOn behalf of,The Editorial Board of Applied Economics and FinanceRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://aef.redfame.com

A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects

In this paper, we propose a process that combines the Risk Matrix approach with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Monte Carlo Simulation for assessing risk factors that have an impact on the duration of a construction project’s activities and predict if it is feasible to terminate the project within the prescribed deadlines. Initially, we identified the risks affecting each task of the specific project, and then, we applied the risk matrix approach for determining the probability and impact of every risk to each activity. The resulting ranking is used to assign uncertainty to activities’ durations and estimate the probability of on-time project completion, employing the Monte Carlo Simulation approach. The main contribution of this paper is the development of an innovative framework that coordinates an established qualitative and quantitative risk classification approach, with a popular multicriteria method and a powerful simulation approach, to effectively predict time deviations while executing complex construction projects under uncertainty. The proposed framework was applied to estimate the possibility of a timely execution of an artificial lake real project on the island of Alonissos, Greece. The analysis results illustrate that this approach clearly could help the project risk manager proactively perform risk mitigation measures while allocating budget and programming a project with a significant impact on the quality of life of residents and tourists of a small island.

Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 8, Number 5 ALI DARUB KASSAR, Univ. of Baghdad, IraqAndrey Kudryavtsev, The Max Stern Yezreel Valley Academic College, IsraelAndualem Ufo Baza, Wolaita Sodo University, EthiopiaIan McFarlane, University of Reading, UKMarco Muscettola, Independent Researcher-Credit Risk Manager, ItalyPayal Chadha, University of Wales Prifysgol Cymru, KuwaitRajeev Rana, APB Govt. P.G. College, IndiaRichard Nguyen, Alliant International University, USASebastian Schich, Organisation for Economic Coopertaion and Development (OECD), FranceVictoria Cociug, Academy of Sciences of Moldova, MoldovaZi-Yi Guo, Wells Fargo Bank, N.A., USA Nikki GibbsEditorial AssistantOn behalf of,The Editorial Board of Applied Economics and FinanceRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://aef.redfame.com

The Management of Na-Tech Risk Using Bayesian Network

In the last decades, the frequency and severity of Natural-Technological events (i.e., industrial accidents triggered by natural phenomena or Na-Techs) increased. These could be more severe than simple technological accidents because the natural phenomenon could cause the prevention/mitigation/emergency systems fail. The dynamic assessment of the risk associated with these events is essential for a more effective prevention and mitigation of the consequences and emergency preparation. The main goal of this study is the development of a fast and dynamic tool for the risk manager. An approach supporting the management of the consequence is presented. It is based on the definition of a risk-related index, presented in the form of a discrete variable that combines frequency and magnitude of the events and other factors contributing to the worsening of Na-Tech. A properly designed Geographical Information System (GIS) allows the collection and processing of territorial information with the aim to create new data contributing to the quantification of the Na-Tech risk index. A Bayesian network has been built which efficiently lends in including within the model multiple elements with a direct or indirect impact on the distribution of risk levels. By means of this approach, a dynamic updating of the risk index is made. The proposed approach has been applied to an Italian case-study.

Importance of Risk Communication and Risk Analysis in Medical Device Industry

In medical device industry the risk management plays a very vital role. There should be proper communication from each and every stakeholder related to risk management of each respective department, it can be Production, Design and Development or Quality Control and all other departments. In this current research work the role of risk analysis which had been done accordingly ISO 14971 for risk management of medical device using FMEA is implemented. FMEA (Failure Mode and Effects Analysis) plays important role in risk analysis by having several steps for mitigation of risk. Also it had been used for identifying hazard of each risk throughout the lifecycle of the medical device. Risk communication should be advanced so, that the risk identified can be easily controlled by taking appropriate risk control measures. In any medical device industry risk analysis should be done properly and as well the risk communication channel should be strong for proper and immediate action. In this research paper practically the role of Risk communication and risk analysis is covered. Risk management of any of the organization can only be effective if the risk analysis is done strongly and the communication related to risk is proper. In this research FMEA analysis for risk analysis is done on a medical device and also the communication from risk manager to the other entire stakeholders of the risk management from various departments are fully taken into the consideration.

The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment—experiences of patients and clinicians

Background Multimorbidity raises the number of essential information needed for delivery of high-quality care in patients with chronic diseases like rheumatoid arthritis (RA). We evaluated an innovative ICT platform for integrated care which orchestrates data from various health care providers to optimize care management processes. Methods The Horizon2020-funded research project PICASO (picaso-project.eu) established an ICT platform that offers integration of care services across providers and supports patients’ management along the continuum of care, leaving the data with the owner. Strict conformity with ethical and legal legislations was augmented with a usability-driven engineering process, user requirements gathering from relevant stakeholders, and expert walkthroughs guided developments. Developments based on the HL7/FHIR standard granting interoperability. Platform’s applicability in clinical routine was an essential aim. Thus, we evaluated the platform according to an evaluation framework in an observational 6-month proof-of-concept study with RA patients affected by cardiovascular comorbidities using questionnaires, interviews, and platform data. Results Thirty RA patients (80% female) participated, mean age 59 years, disease duration 13 years, average number of comorbidities 2.9. Home monitoring data demonstrated high platform adherence. Evaluations yielded predominantly positive feedback: The innovative dashboard-like design offering time-efficient data visualization, comprehension, and personalization was well accepted, i.e., patients rated the platform “overall” as 2.3 (1.1) (mean (SD), Likert scales 1–6) and clinicians recommended further platform use for 93% of their patients. They managed 86% of patients’ visits using the clinician dashboard. Dashboards were valued for a broader view of health status and patient-physician interactions. Platform use contributed to improved disease and comorbidity management (i.e., in 70% physicians reported usefulness to assess patients’ diseases and in 33% potential influence on treatment decisions; risk manager was used in 59%) and empowered patients (i.e., 48% set themselves new health-related goals, 92% stated easier patient-physician communications). Conclusion Comprehensive aggregation of clinical data from distributed sources in a modern, GDPR-compliant cloud platform can improve physicians’ and patients’ knowledge of the disease status and comorbidities as well as patients’ management. It empowers patients to monitor and positively contribute to their disease management. Effects on patients’ outcome, behavior, and changes in the health care systems should be explored by implementing ICT-based platforms enriched by upcoming Artificial Intelligence features where possible. Trial registration DRKS—German Clinical Trials Register, DRKS00013637, prospectively registered. 17 January 2018.