Earticle

본 연구는 소비자 행동 분야의 연구 동향을 구조적으로 파악하기 위해 키워드 네트워크 분석 기법을 적용하였다. 이를 위해 2020년부터 2023년까지 SCOPUS 데이터베이스에 등재된 소비자 행동 관련 학술논문을 수집하고, 주요 키워드, 저자, 소속 국가 등의 서지정보를 기반으로 네트워크를 구축하였다. 분석에는 1-mode 및 2-mode 네트워크 분석, 중심성 및 하위집단 분석이 활용되었으며, 연결중심성, 근접중심성, 매개중심성, 컴포넌트 구조 등을 도출하였다. 연구 결과, 코로나19, 소셜미디어, 머신러닝 등 디지털 환경 변화와 밀접한 키워드들이 높은 중심성을 보였으며, 특정 국가와 연구자들이 네트워크 내에서 핵심적인 연결자 및 매개자로 작용하고 있음을 확인하였다. 또한 소비자 행동 연구는 특정 주제에 편중되지 않고 다양한 클러스터가 병렬적으로 공존하는 확산 구조를 보였다. 본 연구는 소비자 행동 분야의 지식 구조와 흐름을 체계적으로 조망함으로, 향후 정책 수립, 유망 연구 주제 발굴, 다학제 간 협업 전략에 실질적인 시사점을 제공할 것으로 기대된다.

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.

This study examines Indonesian consumers' adoption of Korean smart health-monitoring devices by investigating how perceived benefits and perceived risks influence intention to use, with income as a key moderating variable. Using survey data from 224 respondents and analyzing the model through PLS-SEM, the findings show that performance expectancy, effort expectancy, compatibility, and image significantly enhance perceived benefits, while privacy, security, performance, time, and financial risks substantially increase perceived risk. Both perceived benefits (β=0.431, t=7.319) and perceived risk (β=0.506, t=8.755) strongly predict intention to use, indicating that consumers simultaneously evaluate value and uncertainty when considering these devices. Income significantly moderates these relationships by strengthening the effect of perceived benefits (β=0.129, t=2.041) and reducing the impact of perceived risk (β=- 0.151, t=2.288), indicating that consumers with higher income prioritize functional benefits and are less discouraged by potential risks. Theoretically, this study extends the Net Valence Model to an emerging-market context and demonstrates the importance of socioeconomic differences in benefit and risk evaluation. Practically, the findings highlight the need for differentiated market strategies that emphasize innovation for higher-income consumers and affordability and data-security assurances for lower-income groups.