This paper advances the risk management perspective that superior social performance enhances firm value by serving as an ex ante valuable insurance mechanism. We posit that good social performance is more valuable as an insurance mechanism for firms with higher litigation risks. Moreover, value generation of corporate social performance (CSP) depends on whether a firm has gained pragmatic legitimacy (i.e., a firm's financial health) and moral legitimacy (i.e., whether or not a firm operates in a socially contested industry) among its stakeholders. We find that the value of CSP as insurance against litigation risk is practically significant, adding 2 to 4 percent to firm value. But CSP is less likely to create value if the firm is in financial distress or is operating in socially contested industries.
Background. The relevance of this research is determined by the need to improve the methods of managing complex projects in the military-industrial complex in the face of modern challenges, including geopolitical instability, sanctions pressure and stricter requirements for the implementation of the state defense order. Under the current conditions, traditional approaches to managerial decision-making demonstrate insufficient effectiveness, due to the high degree of uncertainty and the multi-criteria nature of the tasks facing defense industry enterprises. The aim of the research is to develop a scientifically based decision support methodology for optimizing management processes at all stages of the life cycle of creating weapons and military equipment. Matherials and methods. The proposed approach is based on the integration of multicriteria analysis and discrete optimization methods into the decision support system (DSS), which provides comprehensive consideration of technological, resource and time constraints typical of defense industry projects. The methodological basis of the research consists of modern achievements in decision theory, including methods of multi-criteria optimization. Special attention is paid to the development and application of a discrete mathematical apparatus, in particular, the full search method for problems with a limited set of alternatives. The work also uses modern design techniques such as FMEA risk analysis, critical path methods and visualization of project data using Gantt charts. Results. The application of this approach in DSS makes it possible to take into account a set of constraints (resource, time, risk, and production) and find optimal management solutions at various stages of the life cycle of creating IWT samples. The full search method implemented within the framework of the DSS ensures the selection of the best corrective actions even under harsh conditions, which is confirmed by the example of the OCD stage, where the use of weighted estimates and data visualization (Gantt charts) allowed minimizing deviations from the schedule. The proposed methodology increases the validity of decision-making by formalizing the selection process, provides adaptability to changing conditions, and can be integrated into existing project management systems in the defense industry, contributing to their effectiveness. Conclusions. The prospects for further research are related to the integration of artificial intelligence and machine learning methods for predictive analytics in project management. Of particular interest is the development of adaptive decision support systems capable of working in real time and taking into account dynamically changing conditions for the implementation of projects in the military-industrial complex.
The safety of tourists’ health when visiting any tourism business is critical in tourism management and development. By using chaos theory, this study aimed to understand how 408 tourists in Plateau State, Nigeria, behaved toward tourism-related businesses during the COVID-19 pandemic. Completed and retrieved responses were analyzed descriptively using an inductive method, following a pragmatic approach. The findings of this investigation are consistent with chaos theory. Results from our survey indicate that 93% of tourists took precautions. This suggests that in an otherwise random and chaotic period of the tourism crisis, self-organization brought order and new stability. With a mean score of 40%, safety and hygiene were ranked as the two main influencing factors that changed tourists’ actual behaviour during the pandemic. The alteration in behaviour might be attributed to tourists’ awareness of their risk of catching the COVID-19 virus when visiting tourist attractions. Empirical evidence shows that non-interactive restriction techniques had a significant impact on tourist behaviour. Thus, the impact of the pandemic on tourists’ protective behaviour continues to play a part in tourists’ behaviour during and after the pandemic, implying that the chaos theory approach might be used as a crisis management tool in the tourism industry.
In recent years, with the development of science and technology in China, the financial industry has also undergone significant changes. In the diversified financial field, the diversified financial system headed by financial technology gradually occupies a dominant position. The cash of financial technology has played a very important role in improving the efficiency of financial services. However, fintech goes hand in hand with fintech risks. This paper uses AHP + fuzzy comprehensive evaluation model, seeks 50 financial experts to comprehensively quantify the risk of financial industry, and explores the leading factors of China's financial industry risks at present, so as to make predictable intervention. It is found that technical risk, moral risk and legal risk, with a weight of 76% and a fuzzy evaluation index of "high", are the main factors affecting financial technology risks, while traditional financial risks account for the majority but only account for 24%. Although the weight ratio is not large, it still cannot be ignored. The purpose of this paper is to quantify the vague financial industry risks, explore the dominance of financial technology risks and traditional financial risks in the current financial industry, and conclude that in the face of the future development of China's financial industry, it is necessary to pay more attention to intervening in the risks brought by financial technology, so as to optimize resource allocation, but traditional financial risks cannot be ignored.
Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.
The article "Аn inclusive perspective on the management of service institutions along global risk in the context of overcoming the global financial crisis" is dedicated to the main inclusive areas of overcoming financial risks that arose during the Covid-19 pandemic. The article examines the importance and necessity of the hotel business in the cultural, social and economic spheres of the country and the world. There is a parallel between the development of the hotel business and the crisis pandemic in the country. The problems that arose during the pandemic, the threats to business, the consequences that led to financial losses are highlighted. The practical inclusive experience of the world's leading hotel companies was taken into account. The economic factors affecting the state of the hotel business and the ways to overcome them have been studied. Structured management proposals for overcoming financial crises, overcoming existing situations, and ways to prevent them are provided.
The work used a set of research methods that constitute the methodology of analysis and organization of the main publications, legislative acts and laws, namely the methods of generalization, comparison, chronology, analysis and generalization. A feature of the study was the use of the "Petal" histogram to display the digital impact of the Covid-19 pandemic on the level of revenue from hotel services.
The article deals with the urgent issue of saving the hotel business in the period of global crises. The task of researching the management of service institutions during the Covid-19 pandemic in the context of overcoming the global financial crisis has been solved. The world experience of solving problems and inclusive experience in atypical situations that arise when global crises, namely Covid-19, are used.
The totality of factors influencing the activity of the hotel enterprise was studied, and a systematic division into internal and external was carried out. It is explained that the external environment of the organization is a set of elements that are not part of the organization, but have a certain influence on it; to external factors affecting the activity of hotel enterprises: suppliers, inflationary processes in the country, consumers, the legislation of the country, competitors, adjacent markets.
The problem of recovery and business development of enterprises in the tourism and hospitality industry in modern conditions complicated by the Covid-19 pandemic has been identified. Close cooperation and consolidated activities between enterprises of the industry, search for inclusive ways of cooperation, one of which is the creation of combined hotels, are proposed. This form of cooperation in the hospitality industry increases the competitiveness of enterprises in this field, allows closing and restoring medium and large small business enterprises, and ensures their further development at the national level.
Abstract Chemical asphyxiation at petrochemical factories can provoke the unconsciousness or death of factory workers through suffocation. Some chemicals vaporize and mix with air without showing any warning properties that raise the risk of oxygen deficiency. In light of this, Industry 5.0 focuses more on human‐centricity than technology‐driven implementations to ensure secured and work‐friendly environments in industries. Recently, research on factory safety management dependent on the Internet of things (IoT) sensors have been executed unwaveringly. In this work, the ultra‐wideband (UWB) sensor is adopted to recognize the motion and breathing pattern of workers in smart factory scenarios. After capturing the data from the UWB sensor in real‐time, the proposed dataset is further inspected by the deep learning (DL) and traditional machine learning (ML) approaches. Twofold detection schemes are considered where the movement and vital patterns are distinguished first by the stacked ensemble (SE) and the long short‐term memory (LSTM) frameworks. The Bayesian optimized ensemble learning (EL) and bidirectional (Bi‐LSTM) models are further occupied to analyze abnormalities in the breathing rate of a worker in the smart shop floors. The investigated outcome shows that the DL frameworks (LSTM and Bi‐LSTM) outperformed the others by acquiring 99.90% and 99.94% accuracy in 147 s and 293 s, respectively. The devised perception indicates prominent attainment to the smart factory shop floor, Internet of medical things (IoMT), the smart city paradigm, and e‐health appliances.
Vasilescu Gabriel - Victor, Moraru Roland Iosif, Bǎbuţ Gabriel Bujor
Risk management is becoming increasingly more complex. Risk assessment, approached quantitatively, requires a factual database to define the likelihood of adverse health effects of workplace-related injuries and exposures, and it attempts to balance scientific knowledge with concerns of staff, investigators and administration. Practical guidance should be provided for Romanian coal mining companies to make progress in risk assessment process. Guidance is given on how to effectively introduce quantitative risk assessment in mining industry, the main goal being to highlight that the most valuable resource remains experience gained by effectively performing the process. Analyzing how various parameters are described/used, the paper aims to establish the place and role of quantitative risk analysis mining. Possibilities of developing safety/reliability database in coal mining are investigated. The block diagram describing the conceptual structure of a database on failures, safety of equipment and workers in the mining industry was developed. Because mining relies heavily on complex technologies - permanent mining facilities and large mobile equipment and support services - often located in isolated and hostile environments, the implementation of quantitative risk analysis and the development of a realistic database could be considered as a resilience business strategy and conversion of available knowledge into management actions.
The expansion of the breadth of coverage and depth of use of digital finance greatly improves the efficiency of financial services to the real economy, and has an important impact on the economic development of local governments in China. This paper takes the provincial local government debt risk data from 2011 to 2020 as the research sample to deeply investigate the effect and mechanism of digital finance on local government debt risk. The results show that, firstly, digital finance can effectively suppress local government debt risk, and a significant long-term suppressive effect is found by quantile model test. Secondly, in the mechanism of action test, digital finance effectively alleviates local government debt risk through the path of local government fiscal transparency, and has played a significant regulating effect. Finally, based on different regions and development levels, the effect of digital finance on local government debt risk is found to be more significant in eastern regions and regions with high urbanization levels. The findings of this paper provide some theoretical value and practical significance for preventing the occurrence of local government debt risk and regional systemic risk.
Engineering (General). Civil engineering (General), Risk in industry. Risk management
In order to explore the influence of dynamic disturbance on creep behavior of rock, the creep damage evolution in rock under the dynamic disturbance are reproduced with creep damage model. In this model, the constitutive law of rock under combined creep and dynamic loading condition is implemented based on elastic damage principle and Norton-Bailey equation in COMSOL Multiphysics. The numerical results show that the dynamic disturbance alters the creep behavior of rock in three aspects, i.e., time, space and energy. In temporal aspect, dynamic disturbance not only accelerates the creep damage evolution of rock, but also leads to the tensile damage accounting for the main damage mode. The tensile damage mode mainly occurs during the unloading stage of dynamic disturbance, and the tendency of transition becomes evident under more dynamic disturbances. In spatial aspect, the dynamic disturbance may facilitate the development of rock damage, resulting in unstable rock failure. In energy aspect, the dynamic disturbance causes creeping rock to absorb and release energy in a short time, which can be quantified with the energy dissipation rate. The residual deformation caused by the dynamic disturbance is also exponentially related to the energy dissipation rate.
This article draws out some perspectives on the management of product supply chains in the event of a pandemic through cases specific to certain industries: automotive equipment, personal computers (PCs), and home furnishings. In particular, the discussion is based on “distributed management and centralized management of a single location” and the dynamic capability of organizational theory derived from supply chain risk assessment studies. Results show that the automotive industry is shifting to a centralized management model that takes advantage of its inherent closed-integral strengths by increasing proximity to the country of production, while the PC industry is shifting to a model that takes advantage of its global supply chain while maintaining transactions with local suppliers. For the home furnishings industry, results show that “tighter vertical integration” is required.
F. Garzon, É. Bonjour, Jean-Pierre Micaëlli
et al.
Abstract The project management field has the imperative to increase the success probability of projects. Experts have developed several Project Management Maturity (PMM) models to assess project management practices and improve the project outcome. However, the current literature lacks models that allow experts to correlate the measured maturity with the expected probability of success. The present paper develops a general framework and a method to estimate the impact of PMM on project performance. It uses Bayesian networks to formalize project management experts’ knowledge and to extract knowledge from a database of past projects. An industrial case concerning large projects in the oil and gas industry is used to illustrate the application of the method to reduce the risk of project cost (or budget) overruns.
As the impact of climate change and CO2 emissions gain more visibility globally, there is increased interest in understanding how the industry landscape will be reshaped in response to the changing physical, economic and political environment. This paper introduces an approach for quantifying the risks associated with the release and adoption of competitive products and services for the long-term uncertainty generated by climate risk scenarios. Our approach can be used for assessing the risks of business strategies whose revenues are used for the repayment of obligations in industries affected by climate risk or CO2 emission costs, and for estimating the probability of default on those obligations under different scenarios. Our approach can also help gain insight into the uncertainty of different net-zero emission strategies and into the uncertainty of outcomes due to nonlinearities, synergies and model misspecification.
Abstract The automobile sector in India is one the key segment of Indian economy as it contributes to 4% of India’s GDP and 5% of India’s Industrial production. The supply chain of any firm is generally dependent on six driving factors out of which three are functional (information, inventory, and facilities) and 3 are logistic (sourcing, pricing, and transportation). The risk causing factors in supply chains consists of various levels of sub-factors under them. Say for instance, under supply risk, the sub-factors can be poor logistics at supplier end, poor material quality etc., under demand risk, the sub-factors can be inaccurate demand forecasting, fluctuating demand, bullwhip effect, and under logistics risk, the sub-factors can be poor transportation network, shorter lead time, stock outs. Through this study, we observe to find the effect of these factors in the supply chain. We use Failure Mode and Effect Analysis (FMEA) technique to prioritize the various types of risk into zones namely high, medium and low risk factors. Also, we use the Best Worst Method (BWM), a multi-criteria decision-making technique to find out the overall weightings of different risk factors. The combination of these methods can help an organization to prioritize various risk factors and proposing a proper risk mitigation strategy leading to increase in overall supply chain efficiency and responsiveness.
With the promotion of the national transportation power strategy, super large operation networks have become an inevitable trend, and operational safety and risk management and control have become unavoidable problems. Existing safety management methods lack support from actual operational and production data, resulting in a lack of guidance of fault cause modes and risk chains. Large space is available to improve the breadth, depth, and accuracy of hazard source control. By mining multisource heterogeneous operation big data generated from subway operation, this study researches operation risk chain and refined management and control of key hidden dangers. First, it builds a data pool based on the operation status of several cities and then links them into a data lake to form an integrated data warehouse to find coupled and interactive rail transit operation risk chains. Second, it reveals and analyzes the risk correlation mechanisms behind the data and refines the key hazards in the risk chain. Finally, under the guidance of the risk chain, it deeply studies the technologies for refined control and governance of key hidden dangers. The results can truly transform rail transit operation safety from passive response to active defense, improving the special emergency rail transit operation plans, improving the current situation of low utilization of rail transit operation data, but high operation failure rate, and providing a basis for evidence-based formulation and revision of relevant industry standards and specifications.