Management of reputational risk is crucial for financial institutions to establish a solid foundation for strategic decisions, gain customer trust, and enhance resilience against environmental adversities, as they largely operate on digital platforms. Since this becomes even more significant as online transactions and digital interactions amplify the visibility and potential impact of reputational issues in the context of electronic commerce, it is essential to thoroughly investigate environmental factors to achieve a comprehensive understanding of reputational risk. However, measuring and evaluating their influence on reputational risk is challenging due to their inherent connection to human perception. This study aims to explore the factors influencing reputational risk of financial organizations to mitigate potential reputational losses by addressing uncertainties associated with concepts such as vagueness. The employed methodology integrates the Decision-Making Trial and Evaluation Laboratory and Fuzzy Cognitive Map techniques using linguistic fuzzy terms. This approach focuses on both the direct effects of factors on reputational risk and the indirect effects arising from interdependencies between factors. Linguistic fuzzy variables enable us to consider the hesitation of the experts and the vagueness of human judgment. To validate the results, factors are also weighted using the fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method. The most influential factors identified by both methods are market value, revenue, risk culture, shareholder value, firm performance, reputation awareness, and return on equity. Additionally, factors affecting other factors include firm performance, revenue, and growth opportunities.

Project management in the software industry is the process of successfully planning, managing, and completing software development projects. Software projects can often be complex, constantly changing, and full of uncertainties, so project management is of particular importance in the software industry. There are two basic approaches to the execution of software projects. In the waterfall model, the project stages are operated in the determined order and after each stage is completed, the next stage is passed, while in the agile approach, the project is divided into small and priority parts, and the work processes are repeated at regular intervals and improvements are made depending on the feedback received. Recently, there has been a trend towards managing software projects with agile methodologies, as it provides flexibility and adaptability. However, there is no quantitative evaluation of the benefits of agile structuring both in the literature and in the sector, and this benefit is expressed with verbal expressions referring to the formation of a dynamic culture. For this reason, the productivity increase brought by the agile project approach cannot be measured and the benefit it provides cannot be scaled. In this study, a model that takes linguistic uncertainty into account is proposed to quantitatively scale the productivity increase provided by agile project structuring. The proposed model has been applied in a business where enterprise resource planning solutions have been developed and the effect of the transition from waterfall model to agile methodology on the productivity of the project has been investigated. Within the framework of the procedure proposed by the model, expert evaluations were taken in line with the determined quantitative and qualitative criteria, these criteria were listed with the fuzzy SWARA method, and the productivity increase provided by agile methodology was calculated using this information. The findings also allowed us to get an idea of the point of scaling the increase in productivity across the industry.

Fuzzy set theory (FST) is a useful approach for modeling the uncertainties of real-life problems. In some cases, uncertainty level of the events may not be determined surely because of some environmental factors. This is named “incomplete information” case and the hesitation level about the uncertainty is represented with the indeterminacy concept. There are various FST extensions in the literature that consider indeterminacy in modeling. These FST extensions consider similar scenarios, and some parts of the theories overlap with some others so the theories and the nature of considered scenarios must be understood well to obtain reliable results. Nevertheless, most of the studies in the literature do not conceptually analyze the nature of the uncertainty and decides an FST extension as a pre-step of the study without expressing an apparent reason. Therefore, the quality of the obtained results becomes questionable. Moreover, these FST extensions have been developed in line with the requirements of Multi-Criteria Decision-Making (MCDM) problem so, assumptions and the limitations of these theories can cause reliability issues for the fuzzy models of non-MCDM problems and continuous systems. In the scope of this study, capabilities, advantages, and disadvantages of well-known FST extensions that consider indeterminacy are conceptually analyzed and compared in line with the needs of modeling of non-MCDM problems and continuous systems. The analysis has also been illustrated on numerical examples to make findings clear. The analysis showed that, some extensions have clear advantages over others in terms of applicability, ease of calculation and scenario comprehensiveness. This study builds a preliminary step for a guiding approach for the selection of the most reliable FST extension in fuzzy modeling. Besides, it is an invitation to fulfill the gap in the field of fuzzy modeling of non-MCDM problems.