한국시뮬레이션학회논문지 (Journal of the Korea Society for Simulation)

한국시뮬레이션학회 (The Korea Society for Simulation)
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SNS 시장 내 플랫폼 간 다집단 경쟁관계 시뮬레이션 분석

Simulation Analysis of Multi-group Competitive Relationships between Platforms in Social Network Service (SNS) Market

  • 투고 : 2020.09.21
  • 심사 : 2020.10.24
  • 발행 : 2020.12.31
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초록

스마트폰의 빠른 보급과 함께 소셜네트워크서비스(Social Network Service, SNS)의 이용자 수는 매우 빠르게 증가하고 있다. 2018년 기준 전 세계 인구 약 71억명 중 27억명이 SNS를 사용하는 것으로 나타났으며, 대한민국에서는 약 5,100만명 중 약 3,120만 명이 SNS를 사용하는 것으로 나타났다. 이와 같이 지속적으로 성장해온 SNS 시장에 대해 다양한 분석연구가 존재해왔으나, 수리적 모델을 통한 정량적 분석 연구는 미진한 실정이다. 본 연구에서는 국내 SNS 시장에서 가장 큰 비중을 차지하고 있는 페이스북, 인스타그램, 트위터를 대상으로 경쟁 관계 분석을 수행하였다. 본 연구의 목적은 SNS 시장의 흐름과 경쟁관계에 관한 몇 가설을 제시하고 해당 가설을 검정하여 SNS 시장의 경향성을 분석하는 것이다. 경쟁관계분석을 위해 Lotka-Volterra(LV) 모형을 적용하였으며, 시간의 흐름에 따른 경쟁관계 변화를 파악하기 위해 Moving Window 기법을 함께 활용하였다. 제시한 가설을 검정 하기위해 Moving Window 기법을 활용하여 몇 가지 시뮬레이션분석을 진행하였다. 각 플랫폼 간의 상관관계와 시간의 흐름에 따른 경쟁관계의 변화를 확인하였다.

The number of customers on Social Network Services(SNS) is rapidly increasing with the spread of smartphones. As of 2018, about 2.7 billion people of the world population (about 7.1 billion people) and more than 31.2 million people of the total population of South Korea (about 50.1 million) use SNS. There are several studies have been conducted on increasing SNS market. Most of them, however, were not quantitative but qualitative studies. This study is conducted on domestic SNS market to identify the competitive relationship among SNS platforms with great proportion in South Korea, such as Facebook, Instagram and Twitter. The objective is to suggest some hypotheses of the competitive relations, test them, and finally verify the trend of domestic SNS market. Competitive Lotka-Volterra (LV) model is used to find out the competitive relationships and Moving Window is also used to show the changes of them over time. In order to test the hypotheses on the relationships, some experiments are performed with Moving Window technique. Thus, the relations among the platforms and the changes of them over time are identified.

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