Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

Abstract

In a highly intertwined and connected business environment, globalized layout planning can be an effective way for enterprises to expand their market. Nevertheless, conflicts and contradictions always exist between parent and subsidiary enterprises; if they are in different countries, these conflicts can become especially problematic. Internal control systems for subsidiary supervision and management seem to be particularly important when aiming to align subsidiaries’ decisions with parent enterprises’ strategic intentions, and such systems undoubtedly involve numerous criteria/dimensions. An effective tool is urgently needed to clarify the relevant issues and discern the cause-and-effect relationships among them in these conflicts. Traditional statistical approaches cannot fully explain these situations due to the complexity and invisibility of the criteria/dimensions; thus, the fuzzy rough set theory (FRST), with its superior data exploration ability and impreciseness tolerance, can be considered to adequately address the complexities. Motivated by efficient integrated systems, aggregating multiple dissimilar systems’ outputs and converting them into a consensus result can be useful for realizing outstanding performances. Based on this concept, we insert selected criteria/dimensions via FRST into DEMATEL to identify and analyze the dependency and feedback relations among variables of parent/subsidiary gaps and conflicts. The results present the improvement priorities based on their magnitude of impact, in the following order: organizational control structure, business and financial information system management, major financial management, business strategy management, construction of a management system, and integrated audit management. Managers can consider the potential implications herein when formulating future targeted policies to improve subsidiary supervision and strengthen overall corporate governance.

Implementation model of data analytics as a tool for improving internal audit processes

“…This finding is also shared by West et al (2005), who stated that even a fraction of an improvement in prediction quality can result in financial savings (Hu et al, 2021).…”

“…The internal auditor should participate in corporate governance by assessing the internal controls in different areas of the organisation (Singh, 2003), and proposing improvements. This way, the internal audit department contributes to the organisation's governance (Hu et al, 2021) by assessing its internal controls.…”

Understanding the financial innovation priorities for renewable energy investors via QFD-based picture fuzzy and rough numbers

“…First, the problems became increasingly complex, and establishing an effective decisionmaking model has become quite challenging (Kou et al 2021). Different techniques have been used together in the established models to benefit from their combined advantages (Hu et al 2021;Kou et al 2022). In this framework, this study considers the DEMATEL and TOPSIS methods.…”

Albatross analytics a hands-on into practice: statistical and data science application

“…As institutions keep collecting so much data, there is a need to handle the quality of the data becoming more notable by the day. Data Quality Management is the set of measures applied by a technical team or a database management system to enable good new knowledge [22][23][24]. The above collection of techniques is decided to carry out during the data management pathway, from data capture to execution, dissemination, and interpretation [24][25][26].…”

“…Data Quality Management is the set of measures applied by a technical team or a database management system to enable good new knowledge [22][23][24]. The above collection of techniques is decided to carry out during the data management pathway, from data capture to execution, dissemination, and interpretation [24][25][26]. In line with this, the data management is the process of processing, managing, and maintaining data quality [27,28].…”