The first part of this dissertation systematically and comprehensively reviews the literature to understand what is and is not known about Chinese internal migration and mental health. By identifying the research gaps, the second part of this dissertation extends previous research by offering an integrative perspective that articulates how multimodal connectedness by diverse media forms influence one's personal networks, and thus their social capital, social support, and life satisfaction and positive mental health as two subjective well-being outcomes. The theory of media multiplexity (Haythornthwaite, 2005) was employed to explain the relations between Chinese internal migrant workers' diverse media use and their personal ties linked. Media multiplexity advocates that strong ties tend to use diverse media forms to sustain the deep levels of mutual dependency, while only one or two media were employed in the communication of weak ties. However, there is still a dearth of studies investigating Chinese migrant workers' diverse media use to establish and maintain their social ties. In this dissertation, the theory of media multiplexity was advanced with the consideration of the diverse media values in particular Chinese social context within the Chinese migrant worker population. The traditional perspective of media multiplexity theory emphasizes the positive relationship of the media use number and tie strength. In this dissertation I argue that both the number and value of different media forms should be considered to understand the nexus the migrant workers' multiplex media use to interact with different social ties. Moreover, this study also first time investigated the personal networks of Chinese migrant workers and how their personal networks function influences their mental health.
| Identifer | oai:union.ndltd.org:hkbu.edu.hk/oai:repository.hkbu.edu.hk:etd_oa-1525 |
| Date | 11 July 2018 |
| Creators | Liu, Liping |
| Publisher | HKBU Institutional Repository |
| Source Sets | Hong Kong Baptist University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Open Access Theses and Dissertations |
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