메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
이영덕 (University of Tennessee Knoxville) 하세진 (University of Tennessee) 정소원 (인하대학교) 박지선 (인천대학교)
저널정보
한국소비문화학회 소비문화연구 소비문화연구 제27권 제3호
발행연도
2024.9
수록면
69 - 88 (20page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
In-store personalized technology is gaining attention as it transforms consumers’ shopping patterns while supporting seamless omnichannel shopping experiences. In-store personalized technology refers to technology-enabled personalization operated with an in-store digital device (e.g., smart mirrors, assistant robots) for a seamless shopping experience in a physical store. Due to its relative novelty, much remains unknown about the role of personalized technology service (PTS) with past research mainly focusing on conceptualizations of PTS. To enhance current knowledge of PTS in retailing, this study has two purposes. The first is to identify key drivers and barriers important to PTS for in-store shopping. The second is to examine how consumers’ perceptions of the identified PTS driver and barrier factors affect integration, customer engagement, and shopping satisfaction. Two studies were conducted using a web-based survey method with US consumers who have used PTS while shopping in retail stores. In Study 1, an exploratory factor analysis (EFA) reveals five drivers (i.e., hedonic, utilitarian, self-efficacy, co-creation, and synchronicity) and three barriers (i.e., privacy concerns, interaction misfit, and lack of confidence) of PTS. In Study 2, structural equation modeling (SEM) shows overall support for the proposed model. Three drivers (hedonic value, co-creation, and synchronicity) and two barriers (interaction misfit and lack of confidence) of PTS have significant effects on integration. Moreover, integration increases customer engagement, thereby enhancing shopping satisfaction.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

최근 본 자료

전체보기

댓글(0)

0