连续性
新颖性
适度
结构方程建模
产品(数学)
独创性
声誉
隐私保护
心理学
信息隐私
构造(python库)
社会心理学
互联网隐私
计算机科学
数学
机器学习
社会科学
社会学
程序设计语言
创造力
几何学
作者
Xiaobo Mou,Fang Xu,Jia Tina Du
出处
期刊:Aslib journal of information management
[Emerald (MCB UP)]
日期:2021-09-08
卷期号:73 (6): 992-1013
被引量:35
标识
DOI:10.1108/ajim-03-2021-0080
摘要
Purpose The purpose of this study is to explore the effects of recommendation algorithm, product reputation, new product novelty, privacy concern and privacy protection behavior on users’ satisfaction and continuance intention to use short-form video application (APP). Design/methodology/approach Based on the existing theories, the research model of this study was developed and 445 valid data were collected through a questionnaire survey. The partial least squares structural equation modeling (PLS-SEM) was employed for data analysis to test the research model and hypotheses. Findings The results reveal that the recommendation algorithm has a significant positive effect on user satisfaction, new product novelty and privacy concern. The influence of recommendation algorithm on privacy concern is negatively moderated by product reputation. Privacy concern has a significant and positive impact on privacy protection behavior, and privacy protection behavior has a significant and positive impact on user satisfaction. New product novelty also has significant impact on user satisfaction. Originality/value This study is one of the earliest studies to incorporate recommendation algorithm as a construct into the college students’ continuance intention to use short-form video APP. The influence of reputation as a moderator variable on the relationship between algorithm and privacy concerns is also investigated.
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