모바일, PC온라인 매체 방문 행동이 쇼핑 사이트 방문에 미치는 영향에 대한 동태적 연구

다국어 초록 (Multilingual Abstract)

In the e-commerce, the conversion into the multi-media is the important issue. According to the research by Nielsen Korea, the 83% of customers who purchase the products in the e-commerce utilize multi-channel to buy the products such as mobile and online [3]. Thus, to effectively implement online advertising, marketers should understand the customers’ path [15] in the multi-channel. The study of the multi-site activities plays an important role to predict customers’ purchase [28].
To explain the e-commerce site visit activities of customers, we have developed research model in terms of the online advertising. This research model is based on the study of Moe and Fader [23]. There are two types of composition in the research model. First, general site visit as an exploratory search have net effect on the shopping site visit because customers could acquire or develop information on the e-commerce site via online advertising. Secondly, the e-commerce site visit as a goal-directed search cause threshold of the e-commerce site visit because customers could achieve their goal. When the threshold is increased, the probability of a shopping site visit is decreased and vice versa. Thus, we have investigated the impact of customers’ previous visit activities (general site visit and shopping site visit) on the next e-commerce site visit in terms of dynamic view.
Research data was provided by Cheil World Wide. This panel data include mobile and online log data of panelists from Jan. 2013 to March 2013.
As the results, the customers’ e-commerce site visit on the online media would decrease the probability of e-commerce site visit because these visit activities increase the threshold of e-commerce site visit. This result is similar with the previous study [23]. Otherwise, since e-commerce site visit on the mobile media decrease the threshold, the customers’ probability of e-commerce site visit would increase
In summary, the site visit activities on the mobile could improve the probability of e-commerce site visits.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 이론적 배경
• 3. 연구방법
• 4. 분석 결과
• 5. 논의
• 참고문헌

참고문헌 (Reference)

1 정지은, "패션제품에 대한 모바일 구전효과-페이스북을 중심으로-" 한국의류학회 37 (37): 186-201, 2013

2 이홍주, "클릭스트림 데이터를 활용한 전자상거래에서 상품추천이 고객 행동에 미치는 영향 분석" 한국경영과학회 33 (33): 59-76, 2008

3 닐슨코리아, "옴니채널(OMNI-CHANNEL) 소비패턴이 대세, 지금 기업에게 필요한 것은?" 닐슨코리아 2014

4 한국온라인쇼핑협회, "온라인 쇼핑 시장에 대한이해와 전망" 한국온라인쇼핑협회 2013

5 김현교, "소비자 키워드광고 탐색패턴에 나타난 촉진지향성이 온라인 여행상품 구매확률에 미치는 영향" 한국경영과학회 39 (39): 29-48, 2014

6 DMC 미디어, "모바일 쇼핑 이용실태 조사 보고서" DMC 미디어 브랜드 마케팅 팀 2013

7 진정숙, "모바일 마이크로블로깅(Mobile Microblogging) 서비스의 사용요인에 관한 연구" 한국경영과학회 28 (28): 83-94, 2011

8 John, C.N., "Unifying Optimization Algorithms to Aid Software System Users : optimx for R" 43 (43): 1-14, 2011

9 Ghose, A., "Tracking Mobile : The Rise of Smartphones in Marketing" ISB INSIGHT 2014

10 최승훈, "The Interaction Effects of the Shopping Situation and the Product Display Type on the Consumer Response in Mobile Shopping" 한국마케팅학회 14 (14): 119-135, 2012

11 Janiszewski, C., "The Influence of Display Characteristics on Visual Exploratory Search Behavior" 25 (25): 290-301, 1998

12 Manchanda, P, "The Effect of Banner Advertising on Internet Purchasing" 43 (43): 98-108, 2006

13 R Core Team, "R : A language and environment for statistical computing" R Foundation for Statistical Computing, Vienna, Austria

14 Fader, P.S, "Probability Models for Customer-Base Analysis" 23 (23): 61-69, 2009

15 Fader, P.S., "Probability Models for Custemer-Base Analysis" 2009

16 Hui, S. K., "Path Data in Marketing : An Integrative Framework and Prospectus for Model Building" 28 (28): 320-335, 2009

17 Johson, E. J., "On the Depth and Dynamics of Online Search Behavior" 50 (50): 299-308, 2004

18 네이버, "NAVER 2014년 1분기 IR보고서" 네이버 2014

19 Montgomery, A. L., "Modeling Online Browsing and Path Analysis Using Clickstream Data" 23 (23): 579-595, 2004

20 Rutz, O. J., "Modeling Indirect Effects of Paid Search Advertising : Which Keywords Lead to More Future Visits?" 30 (30): 646-665, 2011

21 Park, Y.-H, "Modeling Browsing Behavior at Multiple Websites" 23 (23): 280-303, 2004

22 Luo, X., "Mobile Targeting" 60 (60): 1738-1756, 2014

23 Rutz, O.J, "From Generic to Branded : A Model of Spillover Dynamics in Paid Search Advertising" 48 (48): 87-102, 2011

24 Moe, W.W, "Dynamic Conversion Behavior at E-Commerce Sites" 50 (50): 326-335, 2004

25 DMC 미디어, "DMC 리포트" DMC 미디어 2013

26 Moe, W.W, "Capturing Evolving Visit Behavior in Clickstream Data" 18 (18): 5-19, 2004

27 Moe, W. W., "Buying, Searching, or Browsing : Differentiating Between Online Shoppers Using In-Store Navigational Clickstream" 13 (13): 29-39, 2003

28 IBM, "Black Friday Report 2013" IBM 2013

29 Navarro, D, "An Introduction to the Beta-Binomial Model"

30 Moe, W. W., "An Empirical Two-Stage Choice Model with Varying Decision Rules Applied to Internet Clickstream Data" 43 (43): 680-692, 2006

31 Ghose, A., "An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet" 57 (57): 1671-1691, 2011

32 Schmittlein, D.C., "A Live Baby or Your Money Back : The Marketing of In Vitro Fertilization Procedures" 48 (48): 1617-1635, 2003