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Financial Risk Management Discussion Replies

Financial Risk Management Discussion Replies


Write two Discussions responses:

one: The research aimed to identify how specific financial indicators influenced financial health and the role of risk management in Slovak organizations. Financial risk is imperative in financial institutions. Risk management is imperative because it protects financial institutions from operational, legal, and concentrated dilemmas. Effective risk management will mitigate risk and enhance business functions. Furthermore, risk management aids in identifying, evaluating, and taking the necessary steps to avoid fines, along with reputation and trust being lost in the financial system. The study conducted risk assessments in Slovak institutions. The study aims to identify factors that could be a risk and affect Slovak companies. Slovak is in central Europe and borders Austria, Czech Republic, Hungary, Poland, and Ukraine. The study conducted a regression analysis identifying the relationship between variables (McClave et al.,2021). The statistical test and research design are appropriate because they analyze each country to form a relationship between them. Identifying relationships and trends may aid in solving real-world problems. The researchers obtained data from the Register of financial statements of the Ministry of Finance of the Slovak Republic for 2015.

The data comprised 62,533 companies, and more than 15% experienced financial risks. The research sampled many companies in Slovak, strengthening the chance of depicting the targeted population of Slovak. The 15% were unsuccessful in mitigating risks, therefore leaving the business in a weak state. The researchers analyzed each financial statement for each company and identified 14 financial indicators for each company which were variables in their data set. The companies were divided into two groups: ordinal (default: Unhealthy unsuccessful) and (non-default: healthy, successful). The ratios were determined as such; “if the company has the equity to debts ratio less than 0.4, current ratio L3 is less than 1, and net income is negative, then the company is unhealthy or unsuccessful using the value 1; if those conditions were not met then the company is healthy and successful and marked with the value of 0 (Valaskova et al.,2018). The regression analysis is used to find the relation between the two variables. The dependent variable is the company’s financial health, and the independent variable is the 14 financial ratios. The research results concluded a linear combination (Valaskova et al.,2018). The data used in the analysis determined which companies were bankrupt and which ones were not. A limitation of the study is that the research was conducted with only one European country, Slovak. Future research should analyze the bordering countries around Slovak to analyze the financial risk management in multiple European countries to capture a more reliable study for the European population. The research was valid because the data was from legal business documents (Slovak company financial statements for 2015).


McClave, J., Benson, P. G., & Sincich, T. (2021). Statistics for Business and Economics (14th ed.). Pearson.

Valaskova, K., Kliestik, T., & Kovacova, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105– 121. doi: 10.24136/oc.2018.000

Two: Richards and Kieffer (2023) sought to assess if job satisfaction and mentoring satisfaction influence retention rates for administrators and faculty of associate-level nursing programs.  The authors were looking for a predictive relationship using multiple regression analyses.  The research design used a survey for a cross-sectional quantitative study.  The research used five instruments, including two for mentoring satisfaction, one for job satisfaction, one for retention intention, and a demographic instrument.  The design was appropriate as the authors studied participants across several regions in the United States. (Richards & Kieffer, 2023)

The participants included 108 full-time employees at associate-level nursing schools (Richards & Kieffer, 2023).  Out of the 108 survey responses, only 78 were used, as their surveys were completed in full(Richards & Kieffer, 2023).  Most participants were aged 51 to sixty at 35% (Richards & Kieffer, 2023).  However, 95% of the participants identified as Caucasian or White, and 96% identified as female (Richards & Kieffer, 2023).  Therefore, the target population is limited by the sample population.  It would be difficult to generalize the results of the research findings due to the limited diversity in the sample size. 

There were multiple variables in the research.  The independent variables include mentoring satisfaction and job satisfaction.  The authors used the Mentoring Role Instrument, Satisfaction with Mentor Scale, and the Dimensions of Part-Time Faculty Job Satisfaction to measure the independent variables.  The dependent variable was retention intention, and the authors used the Nurse Educators’ Intent to Stay In Academe Scale.  All instruments used a Likert-type scale; therefore, the data was ordinal.  (Richards & Kieffer, 2023)

There were no studies to confirm the reliability and validity of data results.  The authors also did not explain clearly if the instruments were previously noted as reliable and valid. Instead, the authors stated that while the Satisfaction with Mentor Scale was reported as reliable from statistical research, the instrument was not validated by the original authors of the instrument.  (Richards & Kieffer, 2023)

Richards and Kieffer (2023) used various statistical tests to analyze the data.  First, they performed descriptive statistics.  They then ran a series of correlation coefficient tests, including Pearson’s r and Spearman’s rho, to test the relationships between the independent variables and between the independent and dependent variables.  Additionally, they performed a factorial multivariate analysis of variance using the descriptive statistic variables as the multiple dependent variables.  Finally, the authors ran a multiple regression analysis to assess whether mentoring and job satisfaction could predict retention intentions.  (Richards & Kieffer, 2023)

The results suggested a statistically significant relationship between job satisfaction and retention, and the multiple regression analysis noted that job satisfaction could predict retention in the population.  However, there was no statistically significant relationship between mentoring satisfaction and retention, and the two variables did not correlate within the regression model.  (Richards & Kieffer, 2023)

There were many limitations to the research.  First, the population is limited to associate-level nursing faculty and administrators that identify as Caucasian or White females in their 50s to 60s, living in the western part of the United States.  Second, one of the instruments used was not properly validated, which questions the integrity of the research.  Lastly, the study is limited by design as a cross-sectional quantitative analysis, which does not study participants over time or provide the opportunity for open-ended responses, respectively. (Richards & Kieffer, 2023)


Richards, A. J., & Kieffer, J. (2023). Addressing the associate-level nurse faculty shortage: Do job and mentoring satisfaction predict retention? Teaching and Learning in Nursing, 18(1), 219–224.

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