Conference Agenda

Environmental, Social, and Governance(ESG) initiative is a framework used to assess the environmental and social impact of a company and sustainability, adding the reporting of non-financial information to corporate disclosure. It helps investors make informed decisions that align with their values and long-term goals and also encourages corporations to make strategies for enhancing trust and transparency coping with related risks and even using them as opportunities for business breakthrough. This study aims to identify trends in Environmental, Social and Governance(ESG) issues in Korea and suggest future directions of academic research, corporate management strategies and support policies for ESG. This study explores social connections and meaning structures using a mixed methods of socio-semantic network analysis. The data was collected from news articles covering ESG issues and sustainability reports issued by corporations in Korea over the period from 2004 to 2024. The words in natural language were collected to identify semantic structure and interaction ties between actors were used to figure out social structure by period. Additionally, this study used Latent Dirichlet Allocation (LDA) topic modeling to extract latent themes by period and figure out the changes in the trend of ESG issues. These empirical findings suggest that the ESG issues are predominantly structured around carbon emission in environmental dimensions, human capital and safety issues in social dimensions and transparency in governance dimensions. This study Integrated both social network analysis and semantic network analysis to understand how social connections among actors and knowledge structures influence each other.

This work aims at putting to a joint empirical test the claims of such major social theories as social constructivism and symbolic interactionism. Namely, that shared meaning is established in professional and in everyday interaction, facilitated by material objects in a common physical space.

Data include collaboration and friendship ties, ethnographic descriptions of objects (artworks, materials, tools, and everyday items) filling shared spaces, and fixed individual attributes (e.g., gender and age) for three collectives of artists across three time points.

Not all objects are meaningful to every artist. Hence, any single object usually has no values on what it means to some of the artists. Furthermore, descriptions are usually short, which results in rare overlaps in semantic associations between pairs of descriptions, hindering their direct pairwise comparisons. To address this, based on descriptions of objects provided by both artists in a dyad, we used Correlated Topic Models to evaluate total dyadic similarity in meanings in dyads of artists per group/wave.

Analysing these networks is a modelling challenge due to small size, high clustering, and the nature of similarity relations derived from text and thus systematically constrained. Our pooled MRQAP models across the three groups predict total dyadic similarity from prior similarity, collaboration and friendship ties from the previous wave, and similarity in gender and age. Preliminary results suggest that social ties—particularly collaboration—predict greater similarity of meanings over time. Additionally, social closure (shared contacts) fosters similarity, while attribute similarity also plays a role, though with less consistency.