Financial technology (Fintech) makes a significant contribution to the financial system by reducing costs, providing higher quality services and increasing customer satisfaction. Hence, new studies play an essential role to improve Fintech investments. This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models. Empirical findings are controlled for consistency by applying the VIKOR method. Moreover, we conduct a sensitivity analysis by considering six distinct cases. This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks. Our empirical findings illustrate that results are coherent, reliable, and identify “competitive advantage” as the most important factor among Fintech-based determinants. Moreover, “payment and money transferring systems” are the most important Fintech-based investment alternatives. It is recommended that, among Fintech-based investments, European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations. This is also believed to have a positive impact on the ease of bank’ receivable collection. Another important point is that Fintech-based investments in money transferring systems could help to decrease costs.

https://link.springer.com/content/pdf/10.1186/s40854-021-00256-y.pdf

Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach

Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects

Kano-based mapping of innovation strategies for renewable energy alternatives using hybrid interval type-2 fuzzy decision-making approach

The purpose of this study is to determine appropriate innovative strategies for the renewable energy investments For this purpose, a hybrid multi-criteria decision-making (MCDM) model is proposed based on interval type-2 (IT2) fuzzy sets and alpha cut levels Within this context, hesitant IT2 fuzzy DEMATEL-Based Analytic Network Process (DANP) with alpha cut levels is applied for weighting the customer requirements Moreover, hesitant IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) with alpha cut levels is used for ranking the TRIZ-based strategies priorities of renewable energy investments based on house of quality technique These strategies are also ranked with hesitant IT2 fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach to make a comparative evaluation The results illustrate that ease of access and security are among the most prominent factors of customer requirements for renewable energy investments It is also identified that innovative technical requirement entitled cushion in advance has the best choice for the sustainable renewable energy projects Furthermore, the results of proposed model using hesitant IT2 fuzzy DANP and hesitant IT2 fuzzy TOPSIS are identical for the different levels of alpha cut In addition, it is also defined that the results of hesitant IT2 fuzzy TOPSIS and hesitant IT2 fuzzy VIKOR are quite coherent This is a clear evidence that the proposed model is coherent and could provide comprehensive results for the future studies It is strongly recommended that a detailed analysis is required to identify the risks in renewable energy investment projects Hence, necessary actions can be taken appropriately so that it can be possible to prevent problems before they grow

/pdf/analyzing-the-investments-strategies-for-renewable-energies-17kzfhrhbg.pdf

Analyzing the Investments Strategies for Renewable Energies Based on Multi-Criteria Decision Model

The aim of this study is to generate appropriate strategies to improve renewable energy investments. Within this framework, a novel model has also been proposed which includes three different stages. Firstly, incomplete preferences of the relation matrixes are calculated. For this purpose, 4 different decision makers evaluate the balanced scorecard-based criteria. In this stage, missing values are estimated by incomplete preferences to complete the relation matrixes. Additionally, the second stage includes the computing the fuzzy preferences by considering the consensus-based group decision-making (CGDM). The final stage is related to the calculation of the weights of the criteria by considering Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology. Hence, the main motivation of this study is to identify innovative strategies for the renewable energy investments with a novel multi-criteria decision-making (MCDM) model based on incomplete preferences, CGDM and Pythagorean fuzzy sets. The findings indicate that learning and growth is the most important balanced scorecard-based perspective to improve the performance of renewable energy investments. Additionally, the perspective of internal process is identified as another significant factor for this situation. The biggest problem in renewable energy projects is their high initial costs. Hence, technological developments reduce the production costs of renewable energy sources. Additionally, it is also possible to increase the amount of electricity from renewable energy sources owing to the innovative technologies. Thus, renewable energy investors should follow up-to-date technological developments so that it will be possible to reduce the cost of renewable energy investments.

/pdf/an-extended-pythagorean-fuzzy-approach-to-group-decision-2lcqol70je.pdf

An Extended Pythagorean Fuzzy Approach to Group Decision-Making With Incomplete Preferences for Analyzing Balanced Scorecard-Based Renewable Energy Investments

This study aimed to analyze the systematic risks of wind energy investments. Within this framework, E7 countries are included in the scope of the examination. A large literature review was carried out and 12 different systematic risk factors that could exist in wind energy investments were identified. The analysis process of the study consisted of two different stages. First, the specified risk criteria were weighted with the help of the interval type 2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Second, E7 countries were ranked according to the risk management effectiveness in wind energy investments. In this process, the IT2 fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was taken into consideration. The findings show that volatility in exchange rates and interest rates were the most important risks in wind energy investments. In addition, it was determined that China and Indonesia were the most successful countries in managing risks in wind energy investments. In contrast, India, Russia, and Turkey were determined to be the least successful. Additionally, the IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method was applied as a robustness check of the extended VIKOR method. It was concluded that the ranking results of the IT2 fuzzy TOPSIS method were similar to the results of the IT2 fuzzy VIKOR. It can be understood that the proposed ranking method was consistent with the comparative analysis results. From this point of view, it was observed that countries should take measures regarding their exchange rate and interest rate risks in order to increase the efficiency in wind energy investments. In this context, companies should first ensure that they do not have a foreign exchange short position in their balance sheets by conducting an effective financial analysis. In addition, it is important to use financial derivatives to minimize the exchange rate and interest rate risks. Using these results, it will be possible to manage this risk by taking the reverse position for the existing foreign currency and interest risk. In this way, it will be possible to increase the efficiency of wind energy investments, which will contribute to the social and economic development of each respective country.

https://www.mdpi.com/1996-1073/13/6/1423/pdf

Dejun Qiu

Papers

Multi-faceted analysis of systematic risk-based wind energy investment decisions in E7 economies using modified hybrid modeling with IT2 fuzzy sets