Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A Study on Differences of Experiential Value, Satisfaction, Behavioral Intention Based on Segmented Groups of Tourism SNS Lovemarks Experiences

관광SNS 러브마크경험 세분집단에 따른 이용자의 경험적 가치, 만족 및 행동의도 차이연구

  • Received : 2019.01.18
  • Accepted : 2019.04.20
  • Published : 2019.04.28
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Abstract

The purpose of this study is to present the implications of analyzing users' market segmentation and characteristics based on the tourism SNS lovemarks experience. The results are as follows. First as a result of conducting factor analysis of tourism SNS lovemarks experience, five factors explained namely, mystery, sensuality, intimacy, trust, reputation. Second, as a result of conducting cluster analysis, four cluster groups were identified (reputation/sensuality centered experience group, high lovemarks experience group, intimacy centered experience group, low lovemarks experience group) and each characteristics were profiled. Third, as a result of Chi-square analysis and correspondence analysis, there was a difference in the characteristics of tourism SNS usage by cluster. Fourth, higher tourism SNS lovemarks experience segmented groups were more likely to recognize the degree of experiential value, satisfaction, behavioral intention.

본 연구는 관광SNS 러브마크경험을 근간으로 이용자의 시장세분화 및 특성을 분석하여 시사점을 제시하고자 하였다. 연구의 결과는 다음과 같다. 첫째 관광SNS 러브마크경험에 대한 요인 분석 결과 신비감, 감각성, 친밀감, 신뢰, 평판의 5가지 요인을 도출하였다. 둘째, 군집분석 결과 평판/감각성중심경험집단, 고 러브마크경험집단, 친밀감중심경험집단, 저 러브마크경험집단으로 이루어지는 4개의 군집을 추출하였으며 각 군집별 특징에 대한 상세적 분석을 진행하였다. 셋째, 교차분석과 대응일치분석을 실시한 결과 군집별로 관광SNS 이용 특성에 차이가 있는 것으로 나타났다. 넷째, 관광SNS 러브마크경험을 높게 인식하는 집단일수록 경험적 가치, 만족, 행동의도를 높게 인식하는 것으로 나타났다.

Keywords

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Fig. 1. Correspondence analysis(by visiting area)

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Fig. 2. Correspondence analysis(by SNS platform)

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Fig. 3. Correspondence analysis(by moment of using SNS)

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Fig. 4. Correspondence analysis(by frequency of use tourism SNS)

Table 1. Factor analysis for tourism SNS lovemarks experiences

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Table 2. Factor analysis for experiential value

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Table 3. Cluster analysis

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Table 4. Discriminant analysis on segmentation

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Table 5. Chi-square analysis

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Table 6. Difference analysis

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