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The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

This paper aims to investigate the relationship between gender diversity in CEO positions and FinTech profitability by exploring the moderating role of the average board age on such a relationship.

A unique data set of Italian FinTech companies during the 2017–2019 period was used in an ordinary least square model specification. The model is designed to assess the relationship between the presence of a female CEO and FinTech profitability and the moderating role of the average age of governing board members.

The results of this study indicate that when the average age of the FinTech firm’s board members is relatively low, the profitability of those firms with female CEOs was not significantly different from the profitability of firms with male CEOs. However, among FinTech firms with relatively older board members, the profitability of those firms with a female CEO was lower. This empirical result seems to suggest that older board directors are less prone to recognize female CEO leadership qualities. This supports the need for FinTech firms to adopt good practices in board composition that favor gender inclusion and diversity on board.

The novelty of this study within the literature is that the empirical analysis added new evidence on the relationship between Female CEO and performance by exploring the moderating role of the average age of board members. Moreover, the empirical results of this study suggest specific conditions that could improve the profitability of female-led firms by removing the apparent biased perceptions about the quality of women in leadership among older board members.

This study aims to identify the regions with a high risk of natural disaster, estimate the proportion of households potentially participating in natural disaster insurance and analyze the relationship between disaster risk index and proportion of household potentially participating in natural disaster insurance.

Descriptive and quadrant analysis was applied on the 2019 Indonesia Disaster Risk Index (IRBI) scores and the 2019 National Socio-Economic Survey data.

The results showed there are only two categories of disaster risks in Indonesia based on IRBI categorization: “Medium” and “High.” Some districts in Aceh Province such as Simeuleu, Pidie Jaya and Banda Aceh City were observed to have a high proportion of households potentially participating in the natural disaster insurance while some in Jawa Tengah provinces have fairly low level even though they were categorized as high disaster-prone areas. Moreover, the quadrant analysis showed that 43 districts have high IRBI scores but low insurance participation rates with most discovered to be in Jawa Barat and Sumatera Selatan provinces.

Indonesia does not have a financial mitigation program up to the present time because almost all disaster resolutions are formulated based on emergency funds from the state budget even though it is important to use insurance schemes in all stages of disaster management. To the best of the authors’ knowledge, this study is the first attempt to identify households potentially participating in natural disaster insurance through the National Socio-Economic Survey in Indonesia.