Ethical Consumers’ Awareness of Vegan Materials: Focused on Fake Fur and Fake Leather

Abstract

With an increase in ethical awareness, people have begun to criticize the unethical issues associated with the use of animal materials. This study focused on the transition of global consumers’ awareness toward vegan materials and the relationship between the interest in ethical subjects such as animals, the environment, and vegan materials. For this purpose, consumers’ posts about fur/fake fur and leather/fake leather uploaded on Google and Twitter from 2008 to 2019 were utilized, and the Term Frequency-Inverse Document Frequency (Tf-idf) value was extracted using Python 3.7. Furthermore, the worldwide Google keyword search volume of each word was analyzed using Smart PLS 3.0 to investigate global consumers’ awareness. First, with time, consumers began relating animal materials such as fur and leather to topics such as animal rights, animal abuse, and animal protection. Second, as interest in “animal welfare” increased, interest in “fake fur” also rose, and as interest in “cruelty free” increased, interest in “fake fur”, “vegan fur”, and “vegan leather” also increased. Third, as consumers’ interest in the “environment” increased, interest in vegan materials such as “fake fur” and “fake leather” decreased. However, as interest in “eco” increased, interest in “vegan leather” also augmented.

Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

“…Semantic network analysis is a method of recognizing a word constituting a text as a point (node), connecting the relation between nodes by a line, and grasping the meaning using a visualized connection network [74]. Network analysis is widely used as a method of research in fashion and marketing, as it allows researchers to analyze individuals' opinions and ideas posted on unstructured online boards [66,75,76].…”

“…Based on the results of the CONCOR analysis, NetDraw was utilized to visualize the network. The NetDraw program, a graph visualization software, was used to clearly express the relationships between the words and to visualize the network between the keywords (i.e., sustainable fashion) [66]. Network visualization aims to provide a meaningful visual representation of a network dataset [73].…”

“…Kim et al [24] also identified top actors leading the green consumption network (i.e., eBay Green Team Facebook) and characterized keywords/word-pairs as user messages. Choi and Lee [66] explored consumers' previous and current general awareness of the animal materials and the alternate materials and observed how the interest in environmental rights and animals affect interests in vegan materials applying social big data analysis.…”

“…Calculating the TF-IDF value results in the term "eco-friendly (690.90)" having the highest TF-IDF value. Since TF-IDF indicates the scarcity a word has in a sentence, a word with a higher TF-IDF value can be an important word [23,66]. This is because the word "eco-friendly" is not just a common term that people often use in the social media, but it is considered to be the core of the topic word in terms of "sustainable fashion" at the moment.…”

“…This confirms the previous findings based on term occurrence (frequency). Most words with higher frequencies also ranked higher in TF-IDF values, as the word purification process excluded words that were not needed in the analysis [66]. The keywords are listed in Table 1.…”