Correlational Study: Internal Auditing and Management Control Environment Innovation within Public Sector in the United States

This article is about exploring the relationship between internal human resources auditing and environmental control in the US public sector. The main purpose of this study was to examine the extent to which the determinants of internal audit staff have the potential to influence the predictive value of the public sector control environment. The tools of the work are presented by a quantitative correlation analysis to determine the interdependence of variables (aspects of internal audit personnel: number of staff for each employment, professional development and audit experience of auditors) and resultant indicator (public sector environment). By systematizing literary sources and approaches to solving the problem, as well as using the COSO 2013 Internal Control Framework as a theoretical framework, the authors focus on the question of how much internal audit staffing on employment, professional development and audit experience can influence in the foreseeable future and each of the five COSO components. The empirical analysis was presented in the research, which made it possible to substantiate the following conclusions: the results of six multiple linear regression models did not show statistically significant except for professional development and principle 4 – Personnel recruitment, nonparametric correlation of Kendall's Tau-b correlation is allowed to identify significant interrelationships development and principle set 4, τb = 0.388, p = 0.030. The findings of the study may be useful for implementing further transformational changes in the functioning of the global economy in the context of the growing role and importance of audit and its positive impact on the public sector. The researcher performed an initial examination of the data set to detect missing data and outliers. All incomplete cases and cases with incorrect data were discarded from the dataset. Then the researcher computed the composite variables as means of the items corresponding to each scale or subscale and performed a reliability analysis for all composite measures.