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Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

This study aims to investigate the mediation effects of employee voice and employee well-being on the relationship between relational leadership and organizational citizenship behavior.

This study used a Web-based survey method to collect data from 301 respondents in the four public hospitals of the Sekondi-Takoradi Metropolis. This study used PLS-SEM (WarpPLS) to test the study’s hypotheses.

The findings show that relational leadership has a positive impact on organizational citizenship behavior, and that this link is mediated in part by both employee voice and employee well-being.

This study demonstrates the importance of leaders, paying close attention to employees’ well-being and opinions when attempting to drive organizational citizenship behavior in the health sector.

Based on the review of the extant literature on the impact of leadership on employee behavior and to the best of the authors’ knowledge, it is likely that this study will be the first to show how relational leadership, employee voice, employee well-being and organizational citizenship behavior are related in the health sector, thereby advancing the thrusts of the social exchange and relational leadership theories.