| Author: | Wang, Haoran |
| Title: | Pandemic, technology, and professional well-being : exploring job satisfaction in Hong Kong's conference interpreting industry |
| Advisors: | Xu, Han (LST) |
| Degree: | DALS |
| Year: | 2025 |
| Department: | Faculty of Humanities |
| Pages: | 1 volume (various pagings) |
| Language: | English |
| Abstract: | Job satisfaction is a multifaceted construct encompassing an individual's feelings and attitudes towards their work. It has been extensively studied across various professions due to its significant impact on employee well-being, productivity, and organizational success. In the field of conference interpreting, a profession characterized by high cognitive demands and rapidly evolving technologies, understanding job satisfaction is crucial yet understudied, particularly in Asian contexts. This dissertation examines job satisfaction among conference interpreters in Hong Kong, a unique multilingual and technologically advanced setting. By adopting a Complex Adaptive Systems (CAS) approach, this study aims to provide a more nuanced understanding of the dynamic nature of job satisfaction in this field, offering valuable insights for both theoretical advancements and practical improvements in the interpreting profession. Specifically, this study investigates the interplay between cognitive, affective, and external factors influencing interpreters' job satisfaction, with a particular focus on the impacts of the COVID-19 pandemic and emerging AI technologies. The study addresses significant gaps in existing research by examining job satisfaction as a dynamic concept and exploring the interconnections among factors in the unique multilingual and technologically advanced context of Hong Kong. The research employs a mixed-methods approach, combining quantitative and qualitative data. A questionnaire survey was conducted with 28 conference interpreters based in Hong Kong, followed by semi-structured interviews with 12 interpreters to gain deeper insights into their experiences and perspectives. This methodology allows for a comprehensive examination of the factors shaping interpreters' job satisfaction and their responses to recent global changes. Key findings reveal that interpreters' job satisfaction in Hong Kong exhibits crucial features of Complex Adaptive Systems. The study demonstrates that job satisfaction is not static but dynamic and context-dependent, fluctuating in response to changing circumstances such as the shift to remote interpreting during the COVID-19 pandemic. It is multi-faceted and shaped by various interconnected factors, including cognitive elements, affective components, and external influences such as technological advancements and market dynamics. Moreover, the influence of factors on job satisfaction is non-linear, fluctuating with time and setting, as evidenced by the varying impacts of AI technologies and pandemic-induced changes across different work contexts and career stages. This study contributes to the field by introducing a new theoretical perspective on job satisfaction in interpreting studies and beyond, while providing evidence for the applicability of CAS to job satisfaction research. It offers practical implications for professional development programs, organizational strategies, and policy-making in the interpreting profession. The findings underscore the need for adaptive work environments and proactive strategies to integrate emerging technologies in ways that augment rather than replace human interpreters. While limited by its focus on Hong Kong and small sample size, this research lays a foundation for future longitudinal and comparative studies. It calls for further investigation into the long-term impacts of AI integration on the interpreting profession and the development of comprehensive tools for measuring interpreters' job satisfaction in an evolving professional landscape. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8799.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.18 MB | Adobe PDF | View/Open |
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