사회연결망 분석을 활용한 소비자행동 연구 동향 분석

초록

This study applies a keyword network analysis method to structurally examine research trends in the field of consumer behavior. To achieve this, academic articles related to consumer behavior published between 2020 and 2023 in the SCOPUS database were collected, and a network was constructed based on bibliographic information such as major keywords, authors, and affiliated countries. The analysis employed both 1-mode and 2-mode network techniques, as well as centrality and component analyses, to derive degree centrality, closeness centrality, betweenness centrality, and component structures. The results revealed that keywords closely associated with changes in the digital environment - such as COVID-19, social media, and machine learning - exhibited high levels of centrality. In addition, certain countries and researchers were identified as key connectors and intermediaries within the network. Moreover, the study found that consumer behavior research is not overly concentrated on a single topic but instead demonstrates a diffusion structure in which multiple thematic clusters coexist in parallel. By systematically mapping the knowledge structure and flow in the consumer behavior domain, this study provides practical implications for future policy development, the identification of promising research topics, and the formulation of interdisciplinary collaboration strategies.

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