ABSTRACT
The aim of the article is to compare the impact of selected business risks on the threat of bankruptcy of small or medium-sized enterprises (SMEs) before COVID-19 and during the first wave of the COVID-19 pandemic. 688 SMEs from the business environment of the Slovak Republic participated in the research. Business risks, such as market, financial, personnel, operational and strategic risk were examined. Correlation and regression analysis were used to evaluate formulated hypotheses. The results yielded several significant findings. The three most important business risks before COVID-19 include market, financial and personnel risk according to entrepreneurs. Financial risk is the most significant business risk affecting the threat of bankruptcy in the SME segment in the Slovak Republic after the outbreak of COVID-19. The company's financial performance indicators and the ability of respondents to manage financial risk influence the threat of the company's bankruptcy more strongly during the pandemic. The findings are important for state organizations in mitigating the impacts of the COVID-19 pandemic on the business environment of SMEs in the Slovak Republic, as well as for entrepreneurs themselves and organizations that help SMEs. © 2022, Academic Conferences and Publishing International Limited. All right reserved.
ABSTRACT
Public healthcare institutions are the crucial component in the social and economic development of a nation, particularly India. However, public hospitals in India confront multiple operational risk factors that compromise patient satisfaction. Although all the risk factors are essentially critical, the impact potential of any risk factor is ultimately determined by its ability to induce other risk factors. The current research derives motivation from these scenarios and investigates the characteristics of crucial operational risk factors experienced in the public healthcare sector in a South Indian state. Extensive questionnaire-based surveys were conducted among civilians and healthcare professionals in two phases, i.e., prior to the COVID-19 crisis and during the COVID-19 crisis, for identifying significant risk factors. The collected data is analysed using statistical techniques like exploratory factor analysis (EFA) and partial least squares based structural equation modelling (PLS-SEM) to characterise the inter-relationships between risk factors. The research discloses the translational effect of administrative/infrastructure constraints in public hospitals in compromising the operational performance indirectly through human-related issues rather than having a direct influence. More precisely, the presented model indicates that risk factors like the physical infrastructure limitations and shortage of staff will overburden the existing employees, resulting in human-related issues, including attitudinal issues of employees and community mistrusts and misbelieves. The results reveal seemingly resolvable budget allocation issues, but at the same time alarms the authorities to execute immediate countermeasures. Ultimately, this research seeks to empower public hospital administrators with interesting insights and managerial implications drawn from the statistical models. © 2023, The Author(s), under exclusive licence to Operational Research Society of India.
ABSTRACT
Risk management is important for project success as risks in petroleum projects must be minimized to achieve the goals of production. Risk management composes of planning, identification, analysis, and response, which is an important phase. Controlling risks gains the projects the capability to overcome the uncertainty and thus effectively produce the targeted quantities. In this study, four types of risks have been examined in the oil field, and the Iraqi oil exploration company has been taken as a case study. These four types of risks are operational, financial and administrative, economic and political, and potential risks. The effect of these types has been examined by using a closed-type questionnaire form. The questionnaire form was based on the Likert quintet scale, and it contains 114 risk factors distributed in four groups representing the examined four types of risks. Over 170 questionnaire forms have been distributed in the oil field to engineers, managers, experts, and technicians, and 153 forms have been adopted for the analysis. SPSS software was used to execute the statistical analysis concerning statistical mean and relative important index. The most important factors have been found and then validated by using an assessment checklist. It was found that the most factors affecting operational risks are as follows: the presence of mines and explosives left over from past wars in areas to be explored and the incorrect storage of flammable materials. Regarding the financial and administrative risks, it was found that the existence of financial and administrative corruption in oil companies and the mismanagement by managers or their assistants have the most effect on this type of risk. For the economic and political risks, it was found that the most important factors are the entry of some companies into the blacklist of major economic countries and decreased global market demand for oil. Finally, regarding the potential risks, it was found that the control of terrorist groups over the oil areas and regions is the most significant risk that the Iraqi oil and gas sector may face. In addition, it was concluded that the spread of Coronavirus disease 2019 and other epidemics should be treated as a potential risk because it has a huge effect on global oil prices.
ABSTRACT
Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored. © 2022 IEEE.