What drives students' online self-disclosure behaviour on social media? A hybrid SEM and artificial intelligence approach


Arpaci İ.

INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS, cilt.18, sa.2, ss.229-241, 2020 (SSCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 2
  • Basım Tarihi: 2020
  • Dergi Adı: INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, Communication & Mass Media Index, Communication Abstracts, Compendex, Educational research abstracts (ERA), INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.229-241
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

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.