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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.

Magmoids satisfying the 15 fundamental equations of graphs, namely graphoids, are introduced. Automata on directed hypergraphs are defined by virtue of a relational graphoid. The closure properties of the so-obtained class are investigated, and a comparison is being made with the class of syntactically recognizable graph languages.