ABSTRACT
Businesses are becoming more conscious of operational risk management practices due to the COVID-19 pandemic. However, some firms practice risk management without fully comprehending how it might help them and their needs. As a result, companies that practice risk management without realizing it are being controlled by the discipline itself. The goal of this study is to look into the epistemic process of risk management practice in the workplace. This phenomenological study interviewed 39 risk management officers, executives, and employees. Data are thematically analyzed. This study discovered five epistemic processes of risk mapping using Foucault's governmentality paradigm. This phenomenological study, interestingly, revealed the black box of risk management practices, as well as the behavior of risk management officers, executives, and risk owners who preferred to monitor the compliance aspects of risk management practices rather than comprehend the capabilities of risk management that could be used within their strategic planning process. Unaware of this black box, organizational actors were blanketed by the organization's culture of fear, which created the impression that the authority was always watching every word said and every action taken. Practically, this study contributes an improved understanding of the real function of risk management that helps them justify the practice and reduce unnecessary fear. The paper concludes with limitations and research recommendations.
ABSTRACT
In the new normal era of the COVID-19 pandemic, homestay users are very concerned about security and safety. Risk management, especially operational risk management, plays an important role in mitigating the risk of homestay users. In this research, we determine the effect of operational risk management on the intention to revisit and the intention to recommend homestay users by using satisfaction as a mediating variable. The study was conducted using 198 respondents who have had repeated overnight experiences in homestays operating in the Dieng Plateau, Wonosobo, Central Java, Indonesia. Samples were taken using a multistage sampling technique. The research was carried out using an explanatory sequential design of mixed methods research. Data analysis was performed using Structural Equation Model with Smart PLS 3.0. The results state that operational risk management has a significant positive impact on homestay user satisfaction. Satisfaction has a significant positive impact on the intention to revisit and on the intention to recommend of homestay users. Furthermore, we also show that satisfaction has a significant positive role as a mediating variable from operational risk management on intention to revisit and intention to recommend of homestay users.
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.
ABSTRACT
The Social License to Operate (SLO) and the Value Chain business model are basic elements that need to be considered both at the planning and operation stages of mining operations and in particular in coal mining projects. If a coal mining enterprise loses its SLO, it may face risks in operations, which may lead to value chain risks. One of the causes of enterprise failure as related to coal mining operations is the inability to reliably assess/manage risk holistically and the inability to understand that lack of SLO is a critical risk. Although financial risks are typically assessed for mining projects, lack of SLO risk should also be taken into account starting as early as the bankable feasibility study. Furthermore, as it is difficult to establish a proactive decision-making policy for SLO risk in coal mining operations, the Operational Risk Management (ORM) methodology is probably a good tool to apply towards that goal. For this reason, a Mining Operational Risk Management Model (MORMM) was developed to incorporate risk probabilities and risk severities evaluated by experts. The final risk assessment is coded using Risk Assessment Codes (RACs). A hypothetical scenario was developed utilizing the MORMM model in order to illustrate how risks can be managed during the SLO granting process. This scenario describes a hypothetical coal mining project evaluated by virtual risk evaluators under specific hazard categories. Risk evaluation involves the assessment of risk probability and risk severity. Through this scenario this paper presents ways: (i) to establish a baseline ORM process that will be applicable to any coal mining operation environment, and (ii) to provide a theoretical example to demonstrate how the method can be applied to coal mining operations. The resulting RACs can provide critical information to decision makers regarding the rejection, acceptance or re-engineering of the mining business plan.