期望理论
结构方程建模
心理学
网络效应
技术接受与使用的统一理论
社交网络(社会语言学)
样品(材料)
透视图(图形)
外部性
社会心理学
社会化媒体
计算机科学
经济
统计
数学
微观经济学
化学
色谱法
人工智能
万维网
作者
Emad Abu-Shanab,Mohammed A. Al‐Sharafi,Mostafa Al‐Emran
标识
DOI:10.1080/10447318.2023.2208990
摘要
Despite social networks' prevalence and unlimited benefits, their adoption rates are still unsatisfactory. This cross-country research aims to examine the impact of network externality (NE) and fear of missing out (FOMO) on the continuous use of social networks, which in turn, affects users' self-esteem. To achieve this aim, a conceptual model is developed by extending the unified theory of acceptance and use of technology 2 (UTAUT2) with three new factors: NE, FOMO, and self-esteem. The model is tested using a quantitative research design based on data collected through online surveys from 841 social media users in Qatar and Jordan. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM). The results indicated that the continuous use of social networks is positively affected by performance expectancy (PE), hedonic motivation (HM), and FOMO in both samples. The continuous use is also affected by effort expectancy (EE) in the Jordanian, but not the Qatari sample. In contrast, NE significantly affects the continuous use among Qatari respondents, while this relationship is not supported among their Jordanian counterparts. More interestingly, the continuous use of social networks positively impacts users' self-esteem across the two samples. In summary, this research goes beyond what was examined in the UTAUT2 by investigating the consequences of continuous use on users' self-esteem. The incorporated constructs extend the theoretical perspective of the UTAUT2 by integrating new determinants of the continuous use (i.e., FOMO and NE) and new outcomes of that use (i.e., self-esteem). The reflection of the impact of these factors in a cross-country comparison provides insights into the variation in using social networks between different countries.
科研通智能强力驱动
Strongly Powered by AbleSci AI