Providing an Appropriate Model for Identifying and Evaluating the Factors Affecting the Quality of Internal Controls

Abstract

The purpose of the present study is to propose an appropriate model for identifying and evaluating the factors influencing the quality of internal controls. To achieve this objective, a set of indicators and components related to internal control quality was identified through a review of theoretical foundations and research literature, as well as interviews with experts and professionals in the relevant field. The research approach is mixed-method (qualitative-quantitative) and has been conducted using a multiple grounded theory method. Accordingly, data analysis has been performed at both empirical and theoretical levels. The empirical data were collected through theoretical sampling and 15 semi-structured interviews with experts who had successful experience in implementing internal control systems during the years 2019 and 2023. The theoretical data were obtained through a comprehensive review of the literature. In the findings section, after identifying and extracting the main factors and indicators of internal control quality, a quantitative study was conducted to design the model and to ensure its accuracy and appropriateness. For this purpose, the demographic characteristics of the study sample were first examined, and then the data were summarized using descriptive statistical techniques in tabular format. Subsequently, the designed model of internal control quality was evaluated using confirmatory factor analysis to validate the identified components concerning causal conditions, the core phenomenon, strategies, contextual factors, intervening factors, and the consequences of internal control quality. In the next phase, the proposed model for identifying the factors affecting the quality of internal controls was assessed using the structural equation modeling (SEM) technique within a mixed-methods research approach. The results obtained from the structural equation model fitting in the quantitative section, based on the estimated T-values and p-values, allowed for decision-making regarding the acceptance or rejection of the significance of the relationships between constructs. If the observed T-statistic values in testing the significance of relationships between variables exceed +1.96 or fall below -1.96, or if the p-values for the paths are less than 0.05, the relationships between constructs are considered statistically significant with 95% confidence.