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What drives students’ online self-disclosure behavior on social media? A hybrid SEM and artificial intelligence approach
International Journal of Mobile Communications ( IF 1.522 ) Pub Date : 2020-01-01 , DOI: 10.1504/ijmc.2020.10017999
Ibrahim Arpaci

This study investigated drivers of the online self-disclosure behaviour on social media by employing a complementary structural equation modelling (SEM) and artificial intelligence approach. The study developed a theoretical model based on the 'theory of planned behaviour' (TPB) and 'communication privacy management' (CPM) theory. The predictive model was validated by employing a multi-analytical approach based on the data obtained from 300 undergraduate students. The model focused on the role of security, privacy, and trust perceptions in predicting the attitudes toward the selfie-posting behaviour. The results suggested that privacy and security are significantly associated with the trust, which explains a significant amount of the variance in the attitudes. Consistently, results of the machine-learning classification algorithms suggested that attributes of the security, privacy, and trust could predict the attitudes with an accuracy of more than 61%% in most cases. Further, mediation analysis results indicated that privacy has no direct effect, but an indirect effect on the attitudes. These findings suggested a trade-off between the privacy concerns and perceived benefits of the actual behaviour.

中文翻译:

是什么驱动学生?社交媒体上的在线自我披露行为?混合SEM和人工智能方法

这项研究通过采用互补结构方程模型(SEM)和人工智能方法,调查了社交媒体上在线自我披露行为的驱动因素。该研究基于“计划行为理论”(TPB)和“通信隐私管理”(CPM)理论开发了一种理论模型。基于从300名本科生获得的数据,采用多分析方法对预测模型进行了验证。该模型侧重于安全性,隐私和信任感在预测对自拍照发布行为的态度中的作用。结果表明,隐私和安全性与信任度显着相关,这说明了态度上的很大差异。一致地,机器学习分类算法的结果表明,在大多数情况下,安全性,隐私和信任属性可以预测态度,且准确性超过61 %%。此外,调解分析结果表明,隐私没有直接影响,但对态度有间接影响。这些发现表明,在隐私问题和实际行为的感知利益之间需要进行权衡。
更新日期:2020-01-01
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