The objective of this paper is to assess the influence of various types of crises, including the Subprime, COVID-19, and political crises, on corporate governance attributes, regulations, and the association with bank risk. The consecutive occurrences of crises have significantly impacted the global economy, causing substantial disruptions across various facets of the international banking system. Our hypothesis posits that these crises not only influence governance characteristics and regulations but also impact their correlation with the risk and financial distress experienced by banks. Our study is conducted within the Tunisian context spanning from 2000 to 2021, utilizing a GMM regression on a dataset comprising 221 bank-year observations. Our findings indicate that crises have a discernible effect on the relationship between corporate governance and bank risk, as well as between regulation and bank risk. Our results are strong in a range of sensitivity checks, including the use of alternative proxies to measure the bank risks and corporate governance metrics.

In a rapidly evolving digital economy, cyberpreneurship has emerged as a pivotal force driving innovation and economic growth. The study applies the Theory of Planned Behaviour in predicting entrepreneurial intention in the context of Malaysia, where the government has actively championed digital entrepreneurship. Drawing from a sample of 473 final-year university students in the Klang Valley region of Malaysia, the study investigates the impact of Individual Entrepreneurial Orientation (IEO) dimensions, namely innovativeness, risk-taking, and proactiveness, on the intention to engage in cyberpreneurship within the context of Digital Free Trade Zones (DFTZ). The study further examines the moderation effect of psychological characteristics incorporating visionary thinking, self-efficacy, opportunism, and creativity to provide a comprehensive understanding of the factors influencing cyberpreneurial intentions. With the moderating variable, the paper presents a comprehensive model to investigate the IEO and psychological characteristics contributing to cyberpreneurship intentions and its impact on engagement in DFTZ. An empirical examination of data and hypotheses found that risk-taking (RISK) and proactiveness (PRO) are significantly related to cyberpreneurial intention. Psychological characteristics significantly proved its moderating role in its interaction with innovatiness (INNO), risk-taking (RISK), and proactivness (PRO) in influencing cyberpreneurial intentions (CYBER_PI). Innovativeness (INNO) without the influence of the moderating variable is not significantly related to cyberpreneurial intentions. Engagement with the Digital Free Trade Zone (DFTZ) through the mediating role of cyberpreneurial intentions (CYBER_PI), the innovativeness (INNO) did not succeed. On the other hand, risk-taking (RISK) and proactiveness (PRO) are found to be significant. The paper contributes to the landscape of e-commerce and digital trade literature by advancing our understanding of the factors driving individuals’ intentions to participate in cyberpreneurship and engage in DFTZ. The findings of this study provide valuable insights for policymakers, educators, and entrepreneurs alike.

The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

Finance is the core of the modern economy and the bloodline of the real economy; adherence to the people-centered value orientation and the financial services of the real economy as the fundamental purpose is an important connotation of the road of economic development with Chinese characteristics. Financial work is distinctly political and people-oriented, and must consciously practice the concept of the people, serve agricultural and rural development and farmers to increase their income and contribute to the common prosperity of farmers and rural areas. This study is based on the key factors affecting the multidimensional poverty of rural households—external rural financial resources availability and internal rural household entrepreneurship, rural household risk resilience, and rural household financial capability joint analysis. Based on financial exclusion theory, financial inclusion theory, poverty trap theory, and financial literacy theory, to build a logical framework between the rural financial resources availability, farmers’ financial capability, farmers’ entrepreneurship, farmers’ risk management capability, and farmers’ poverty, and then empirically explore the optimization mechanism of poverty reduction for farmers, and analyze the heterogeneity of the financial resources availability, to reduce the return to poverty caused by the lack of entrepreneurial motivation and the low level of risk resilience of rural households. The study aims to improve the farmers’ financial capability and promote sustainable and high-quality development of rural households. In this study, we modeled financial resource availability and rural household poverty using structural equations and surveyed rural households using a scale questionnaire. It was found that financial resource availability significantly affects rural household risk resilience, farmers’ entrepreneurship, and rural household poverty and that rural household risk resilience significance mediates the relationship between financial resource availability and rural household poverty, financial capability plays a significant moderating role. However, the mediating effect of farmers’ entrepreneurship on the availability of financial resources and farmers’ poverty is insignificant. Here, we put forward corresponding countermeasures and recommendations: guiding the allocation of financial resources to key areas and weak links; optimizing financial services; and building a long-term mechanism.

Noise pollution in construction sites is a significant concern, impacting worker health, safety, communication, and productivity. The current study aims to assess the paramount consequences of ambient noise pollution on construction activities and workers’ productivity in Peshawar, Pakistan. Noise measurements have been recorded at four different construction sites in Peshawar at different times of the day. Statistical analysis and Relative Importance Index (RII) are employed to evaluate the data Risk variables, such as equipment maintenance, noise control, increased workload, material handling challenges, quality control issues, and client satisfaction. The results indicated that noise levels often exceeded permissible limits, particularly in the afternoon, posing significant worker risks. In addition, RII analysis identified communication difficulties, safety hazards, and decreased productivity as significant issues. The results show that noise pollution is directly linked with safety risks, decreased performance, and client dissatisfaction and needs immediate attention by authorities. This paper proposes a strategic policy framework, recommending uniform hand signals and visual communication methods without noise for workers, worker training about safety, and using wearable devices in noisy settings. Communication training for teams and crane operators, proactive quality control, and customer-oriented project schedules are also proposed. These recommendations aim to mitigate the adverse effects of noise pollution, enhance construction industry resilience, and improve overall operational efficiency, worker safety, and client satisfaction in the construction sector of Peshawar, aligning with policy and sustainable development objectives.