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
Suh, A-Young (College of Business Administration, Ewha Womans University) Shin, Kyung-Shik (College of Business Administration, Ewha Womans University) Lee, Ju-Min (Kyung Hee Cyber University)
저널정보
한국경영정보학회 Asia pacific journal of information systems Asia pacific journal of information systems 제20권 제1호
발행연도
2010.1
수록면
57 - 79 (23page)

이용수

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

초록· 키워드

오류제보하기
In a virtual community, one can possess multiple identities and pretend to be different by creating self-identity in contrast with his or her actual self. Does false identity undermine the qualitative growth of a virtual community by reducing members' accountability? Or does it stimulate their contributive behaviors by ensuring freedom of speech? It is imperative to understand the effects of multi-identity considering the distinct properties of a virtual community in which people easily change their identities at little or no cost. To answer these questions, we adopted the concept of self-discrepancy from the social psychology theory rooted in the concept of the self and developed a theoretical model to predict quality of contribution of the individual member in virtual communities. Based on the self-discrepancy theory, we first identified two different domains of the self: (1) an "actual self" that consists of attributes that the person believes he or she currently possesses in real life and (2) a "cyber self" that consists of attributes the person believes he or she possesses in a virtual community. Next, we derived an index for two different types of self-discrepancy by using the differences between the actual and the cyber identities: Personal Self-discrepancy and Social Self-discrepancy. Personal Self-discrepancy reflects the degree of discrepancy between actual and cyber identity regarding a person's intelligence, education, and expertise. Social Self-discrepancy reflects the degree of discrepancy between actual and cyber identity regarding a person's morality, sociability, and accordance with social norms. Finally, we linked them with sense of virtual community, perceived privacy rights, and quality of contribution to examine how having a multi-identity influences an individual's psychological state and contributive behaviors in a virtual community. The results of the analysis based on 266 respondents showed that Social Self-discrepancy negatively influenced both the Sense of Virtual Community and Perceived Privacy Rights, while Personal Self-discrepancy negatively influenced only Perceived Privacy Rights, thereby resulting in reduced quality of contribution in virtual communities. Based on the results of this analysis, we can explain the dysfunctions of multi-identity in virtual communities. First, people who pretend to be different by engaging in socially undesirable behaviors under their alternative identities are more likely to suffer lower levels of psychological wellbeing and thus experience lower levels of sense of virtual community than others. Second, people do not perceive a high level of privacy rights reflecting catharsis, recovery, or autonomy, even though they create different selves and engage in socially undesirable behaviors in a virtual community. Third, people who pretend to be different persons in terms of their intelligence, education, or expertise also indirectly debase the quality of contribution by decreasing perceived privacy rights. The results suggest that virtual community managers should pay more attention to the negative influences exercised by multi-identity on the quality of contribution, thereby controlling the need to create alternative identities in virtual communities. We hope that more research will be conducted on this underexplored area of multi-identity and that our theoretical framework will serve as a useful conceptual tool for all endeavors.

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!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0