The strengthening of corporate governance based on applied fuzzy logic

Abstract

Despite advances in research on corporate governance, there are still knowledge gaps. The article aims to broaden the debate on strengthening corporate governance and propose a tool to facilitate decision‐making. The research is novel because of the engagement of 117 master's students from a business school, contributing to competency‐based learning on Good Corporate Governance (GCG). This is applied research with the explanatory objective and quantitative approach through modeling and simulation. The OWA Operator assesses GCG principles' adequacy and reveals a ranking of 28 enterprises listed on the Lima Stock Exchange in corporate governance, increasing transparency and reducing risks. The results indicate that six enterprises would be solid in the GCG, and the corporate governance should consider strategic capability, fair dealing, transparency, and good social responsibility practices. The main contributions are reducing the identified knowledge gaps and proposing actions to strengthen governance. The authors suggest promising lines of research.

Algorithm Applied to SDG13: A Case Study of Ibero-American Countries

“…Their effectiveness also responds to new societal needs, such as sustainable development [20], climate change [30], and the COVID-19 pandemic [31]. In the case of the OWA operator, it is a practical algorithm for evaluation and prioritization [32]. It is also flexible in the modeling and simulation process since it is defined by a vector of weights and not by a single parameter [32].…”

“…Scientists applied the OWA operator to facilitate decision-making by public managers in sustainable transport [34]. Another study evaluated companies listed on the Lima Stock Exchange considering the level of compliance with the Principles of Good Corporate Governance [32] and in the decision-making of consumers in the city of Barcelona regarding the selection of groceries by neighborhoods that best suit their needs [35]. Finally, the literature review of this article also showed few publications of the OWA operator applied to climate change, evidencing the fifth knowledge gap and an opportunity for academic contribution.…”

“…Finally, the literature review of this article also showed few publications of the OWA operator applied to climate change, evidencing the fifth knowledge gap and an opportunity for academic contribution. As a drawback, this algorithm depends on the quality of the information received [32], and to overcome this problem, the authors will use data from official sources to perform the calculations. In addition, the results will be validated by three academic specialists from Latin American universities.…”

“…In the case of the OWA operator, it is a practical algorithm for evaluation and prioritization [32]. It is also flexible in the modeling and simulation process since it is defined by a vector of weights and not by a single parameter [32]. Other researchers claim that this aggregation operator helps merge numerical information and decision-making problems [33].…”

“…In addition, the case study based on official data is an empirical investigation of a contemporary phenomenon within its actual context [38]. Lastly, the combined research method supports model validation and generates interesting theoretical and practical implications [32]. In this context, the research aims to propose an algorithm to evaluate SDG13 indicators and to deepen the discussion on climate change.…”