Predictors of online identity reconstruction from an advanced self-discrepancy theory perspective / Chuan Hu

Chuan , Hu (2019) Predictors of online identity reconstruction from an advanced self-discrepancy theory perspective / Chuan Hu. PhD thesis, Universiti Malaya.

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      In face-to-face communication, to avoid sanctions and disapproval from others, most people choose to hide the negative aspects of their true self (such as socially undesirable personalities, beliefs, and consciousness) that conflict with the social norms and laws. In contrast, in an anonymous online environment (e.g., anonymous social network platforms), people feel less restrained and can express their true self more freely and openly. When interacting with others online, people can reconstruct an identity that is partly or even completely different from their physical identity, namely online identity reconstruction. However, whether people reconstruct their online identity based on their true self is not yet clear. In addition, people have been spending increasingly more time online every day, particularly on social network platforms. With the various social network platforms available, people can freely choose to use any of them and switch to others as they wish. Therefore, it is important for service providers to improve user satisfaction and retain users. Even though existing research has investigated online user satisfaction from different perspectives, there is still a lack of research on the effects of online identity reconstruction on user satisfaction. Using the sequential exploratory design, I conducted qualitative research in the first phase to explore the role of the true self in online identity reconstruction. In the qualitative phase, data were collected from 57 participants through interviews. Content analysis revealed four factors (anonymity, less restraints, online-offline dissociation, and online listeners) that motivate people to involve their true self as a part of their self-guides online and express more of their true self when reconstructing their identity in an anonymous virtual environment. By incorporating the true self as an important part of an individual‘s self-guide and identity, the qualitative study advanced the self-discrepancy theory, making it more comprehensive for anonymous environments. Built on the results of the qualitative phase, quantitative research was conducted in the second phase to investigate the effects of identity reconstruction on user satisfaction. Given that people can reconstruct an online identity on the basis of their own ideas, it is likely that online identity reconstruction fulfills their psychological needs, thereby, affecting their satisfaction. Drawing upon the advanced self-discrepancy theory and the framework of psychological well-being, I built a theoretical model in the quantitative phase to test how online identity reconstruction (which includes three domains of the self: the ought self, the ideal self, and the negative true self) affects people‘s need for autonomy and self-acceptance (which are highly related to online identity reconstruction), and further influences users‘ overall satisfaction with social network platforms. The quantitative data (n = 837) were collected from QQ, a social network platform in China. All the main effects in the research model were supported. In particular, online identity reconstruction on social network platforms was found to be positively associated with people‘s need for autonomy and self-acceptance. Additionally, the self-acceptance level and autonomy exhibited a positive relationship with user satisfaction. This study makes various contributions to the literature and practice.

      Item Type: Thesis (PhD)
      Additional Information: Thesis (PhD) – Institute of Advanced Studies, Universiti Malaya, 2019.
      Uncontrolled Keywords: Identity reconstruction; Negative true self; Advanced self-discrepancy theory; Self-determination theory; Users‘ satisfaction
      Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
      Divisions: Faculty of Computer Science & Information Technology
      Depositing User: Mr Mohd Safri Tahir
      Date Deposited: 26 Apr 2021 02:39
      Last Modified: 10 Jan 2022 06:54

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