Informatica

Green communication is important for businesses to achieve customer satisfaction and gain a significant competitive advantage. Therefore, improving the indicators is very significant for increasing the green communication performance of businesses. However, these improvements cause cost increase for businesses. Hence, there is a significant need for a priority analysis on the variables that will affect the green communication performance of businesses to use the budget more effectively. The purpose of this study is to evaluate important indicators of effective green communication for the companies. For this purpose, a novel model is proposed that has mainly two different parts. In this process, the evaluations of three decision makers are taken into consideration. At the first stage, selected indicators are examined by using artificial intelligence-based sine trigonometric Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL). Secondly, emerging seven countries are ranked according to the performance of the green communication. In this context, artificial intelligence-based sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) technique is taken into consideration. Moreover, these countries are also ranked by using additive ratio assessment (ARAS) methodology to make a comparative evaluation. The main contribution of this study is that artificial intelligence methodology is integrated with the fuzzy decision-making model. Artificial intelligence methodology is considered to generate decision matrix. With the help of this situation, more appropriate calculations can be made. Proposing RATGOS methodology to the literature by the authors is another significant contribution of this proposed model. To overcome criticisms regarding the existing ranking decision-making techniques in the literature, RATGOS model is generated by making computations with geometrical mean. Owing to this issue, it can be possible to reach more effective solutions. The findings demonstrate that informativeness is the most crucial issue for the improvement of green communication performance of the companies. Meeting customer expectation is another important situation that should be taken into consideration in this manner. Considering these findings, it would be appropriate to establish sectoral standards and guidelines to provide information in green communication. Thanks to these standards, it is possible for companies to provide detailed and comprehensive information to their customers. The ranking results of both RATGOS and ARAS are the same that gives information about the consistency and coherency of the proposed model. The ranking results indicate that China and Russia are the most successful emerging countries with respect to the green communication performance.

This paper attempts to demystify the stability of CoCoSo ranking method via a comprehensive simulation experiment. In the experiment, matrices of different dimensions are generated via Python with fuzzy data. Stability is investigated via adequacy and partial adequacy tests. The test passes if the ranking order does not change even after changes are made to entities, and the partial pass signifies that the top ranked alternative remains intact. Results infer that CoCoSo method has better stability with respect to change of alternatives compared to criteria; and CoCoSo method shows better stability with respect to partial adequacy test for criteria.

In this study, effect of the environmental factors on the organizational behaviour in higher education sector is analysed and these factors are prioritized. For this aim, first, the environmental criteria affecting the organizational behaviour of higher education sector are selected from the literature. Then, as a solution methodology, (i) some experts are asked to determine pairwise comparison of the criteria, (ii) the linguistic terms are converted to interval-valued Pythagorean fuzzy values, and (iii) an interval-valued Pythagorean fuzzy DEMATEL approach is developed and applied. According to the results, most of the economic, political, and professional domain criteria are of the cause category.

The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples.

Multiple Criteria Decision-Making (MCDM) is one of the most reliable and applicable decision-making tools to address real-life complex and multi-dimensional problems in accordance with the concepts of sustainable development and circular economy. Although there have been several literature reviews on several MCDM methods, there is a research gap in conducting a literature review on the Multi-Attributive Border Approximation area Comparison (MABAC) as a useful technique to deal with intelligent decision-making systems. This study attempts to present a comprehensive literature review of 117 articles on recent developments and applications of MABAC. Future outlook is provided considering challenges and current trends.

During the COVID-19 pandemic, masks have become essential items for all people to protect themselves from the virus. Because of considering multiple factors when selecting an antivirus mask, the decision-making process has become more complicated. This paper proposes an integrated approach that uses F-BWM-RAFSI methods for antivirus mask selection process with respect to the COVID-19 pandemic. Finally, sensitivity analysis was demonstrated by evaluating the effects of changing the weight coefficients of the criterion on the ranking results, simulating changes in Heronian operator parameters, and comparing the obtained solution to other MCDM approaches to ensure its robustness.