The purpose of the study is to investigate how environmental concern, eco-labelling, influencers and user-generated content affect Generation Z’s green purchase intention. The objective of this study is to contribute with a new scope that combines influencers and user-generated content on digital platforms with environmental concern for Generation Z. The study also aims to add new value in predicting Generation Z’s green purchase intention and results that can be implemented in future marketing strategies. To test the framework, a quantitative research approach, with an online survey, was applied to collect data from Generation Z. The sample size consisted of 393 individuals from Generation Z. Structural Equation Modelling was applied to test the hypothesized framework. All hypotheses were accepted, and hence, this research has identified key variables to predict Generation Z’s green purchase intention. Additionally, this paper found that environmental concern has a significant positive impact on Generation Z’s user-generated content and eco-labelling, and influencers positively affect Generation Z’s user-generated content. This study can aid companies that employ an influencer marketing approach to comprehend how they can motivate customers to buy sustainable products more frequently. This study provides crucial and valuable insights into further understanding how the sustainable consumption behavior of Generation Z can be impacted by the utilization of influencer marketing and their concern for the environment. It also provides a deeper understanding of how influencers and their perceived concerns for the environment can be combined with user-generated content and eco-labelling, as well as subsequent effects on the green purchase intention of members of Generation Z.

This paper presents a comprehensive study of the linguistic Z-graph, which is a novel framework designed to analyze linguistic structures within social networks. By integrating concepts from graph theory and linguistics, the linguistic Z-graph provides a detailed understanding of language dynamics in online communities. This study highlights the practical applications of linguistic Z-graphs in identifying central nodes within social networks, which are crucial for online businesses in market capture and information dissemination. Traditional methods for identifying central nodes rely on direct connections, but social network connections often exhibit uncertainty. This paper focuses on using fuzzy theory, particularly linguistic Z-graphs, to address this uncertainty, offering more detailed insights compared to fuzzy graphs. Our study introduces a new centrality measure using linguistic Z-graphs, enhancing our understanding of social network structures.

The selection of environmentally sustainable suppliers has been a significant challenge in management decision-making (DM). Multicriteria group decision-making (MCGDM) is a ranking methodology used to select suppliers, but it is complex and influenced by the different opinions of decision-makers. Once again, extensive research on MCGDM has exposed inadequacies in the trustworthiness of experts’ judgements, which profoundly impact the ultimate ranking results. The Pythagorean linguistic number (PLN) concept has been used to address MCGDM by considering experts’ confidence levels and real-world scenarios. This study introduces an extensive technique using a quantum scenario-based Bayesian network (QSBN) and Deng entropy-based belief entropy to account for the interference of beliefs. The goal is to replicate the subjectivity of experts’ opinions during different stages of DM, including the accumulation of experts’ weights and alternative probabilities. The correlation coefficient of PLNs is introduced for determining criterion weights and employing new techniques based on entropy methods for experts’ weights. The MULTIMOORA approach consolidates the probability of alternatives in QSBN among all experts, and the interference value is computed using belief entropy, an index for quantifying the probability of uncertainty. The study provides a numerical example to illustrate the proposed methodology, specifically focusing on selecting environmentally sustainable suppliers, and demonstrates its applicability and effectiveness.

Directed fuzzy hypergraphs are introduced as a generalization of both crisp directed hypergraphs and directed fuzzy graphs. It is proved that the set of all directed fuzzy hypergraphs can be structured into a magmoid with operations graph composition and disjoint union. In this framework a notion of syntactic recognition inside magmoids is defined. The corresponding class is proved to be closed under boolean operations and inverse morphisms of magmoids. Moreover, the language of all strongly connected fuzzy graphs and the language that consists of all fuzzy graphs that have at least one directed path from the begin node to the end node through edges with membership grade 1 are recognizable. Additionally, a useful characterization of recognizability through left derivatives is also achieved.

Decision makers (DMs) are often viewed as autonomous in the majority of multicriteria group decision making (MCGDM) situations, and their psychological behaviors are seldom taken into account. Once more, we are unable to prevent both positive and negative flows of varying alternative preferences due to the nature of attributes or criteria in complicated decision-making problems. However, DMs’ perspectives are likely to affect one another in complicated MCGDM issues, and they frequently use subjective limited rationality while making decisions. The multicriteria quantum decision theory-based group decision making integrating the TODIM-PROMETHEE II strategy under linguistic Z-numbers (LZNs) is designed to overcome the aforementioned problems. In our established technique, the PROMETHEE II controls the positive and negative flows of distinct alternative preferences, the TODIM method manages the experts’ personal regrets over a criterion, and the quantum probability theory (QPT) addresses human cognition and behavior. Because LZNs can convey linguistic judgment and trustworthiness, we provide expert LZNs for their viewpoints in this work. We determine the criterion weights for each expert after first obtaining their respective expert weights. Second, to represent the limited rational behaviors of the DMs, the TODIM-PROMETHEE II approach is introduced. It is employed to determine each alternative’s dominance in both positive and negative flows. Third, a framework for quantum possibilistic aggregation is developed to investigate the effects of interference between the views of DMs. The views of DMs are seen in this procedure as synchronously occurring wave functions that affect the overall outcome by interfering with one another. The model’s efficacy is then assessed by a selection of renewable energy case studies, sensitive analysis, comparative analysis, and debate.

COVID-19 is a contagious disease that poses a serious risk to public health worldwide. To reduce its spread, people need to adopt preventive behaviours such as wearing masks, maintaining physical distance, and isolating themselves if they are infected. However, the effectiveness of these measures may depend on various factors that differ across countries. This paper investigates how some factors, namely outsiders’ effect, life expectancy, population density, smoker percentage, and temperature, influence the transmission and death rate of COVID-19 in ninety-five top-affected countries. We collect and analyse the data of COVID-19 cases and deaths using statistical tests. We also use fuzzy logic to model the chances of COVID-19 based on the results of the statistical tests. Unlike the conventional uniform weighting of the rule base in fuzzy logic, we propose a novel method to calculate the weights of the rule base according to the significance of the factors. This study aims to provide a comprehensive and comparative analysis of the factors of COVID-19 transmission and death rates among different countries.