互动性
独创性
结构方程建模
心理学
超链接
身份(音乐)
社会认同理论
广告
社会心理学
互联网隐私
万维网
计算机科学
网页
业务
创造力
社会团体
物理
机器学习
声学
出处
期刊:Internet Research
[Emerald (MCB UP)]
日期:2012-06-01
卷期号:22 (3): 252-274
被引量:220
标识
DOI:10.1108/10662241211235644
摘要
Purpose The purpose of this paper is to examine the effects of interactive and social features on users' online experiences and their purchase intention of virtual goods from a social network site. Design/methodology/approach A banner with a hyperlink that connected to the author's web survey was posted on the homepage of Facebook. Of the 258 responses returned, 176 were fully completed. Measurement items were adapted from previous literature. Structural equation modeling (SEM) was used to evaluate the research model and hypotheses testing. Findings The results of an empirical study supported the use of the stimuli‐organism‐response (S‐O‐R) model in a social networking site and showed how environmental features should be incorporated to enhance users' online experiences and purchase intentions. Specifically, social identity showed the strongest influence on involvement and flow. More specifically, affective involvement showed the greatest influence on purchase intention compared to flow and cognitive involvement. Practical implications The relative importance of both interactivity and social identity in platform features in shaping consumers' online experiences should not be ignored. The author suggests online games or apps. Additionally, platform providers should advance social identity features that show a strong positive impact on users' online experiences. Originality/value With the proliferation of online social gaming, there is growing evidence for virtual goods consumption; however, relatively few studies have discussed this phenomenon. This paper draws on hypotheses from environmental psychology; specifically, users' intentions to purchase are modeled on user responses to the online stimuli of a Web platform and the online experience that such an environment elicits.
科研通智能强力驱动
Strongly Powered by AbleSci AI