Factors affecting bridge employment behavior: Surveying Chinese older adults as anchors in social media
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Received August 1, 2024;Accepted September 27, 2024;Published October 24, 2024
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Author(s)Lingzhi Liu ,Link to ORCID Index: https://orcid.org/0000-0003-1221-5909Link to ORCID Index: https://orcid.org/0000-0001-7619-8605
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DOIhttp://dx.doi.org/10.21511/ppm.22(4).2024.09
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Article InfoVolume 22 2024, Issue #4, pp. 108-124
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Creative Commons Attribution 4.0 International License
Social media has brought new opportunities to bridge employment and has become an essential channel for addressing the issue of an aging society. This study aims to explore the factors influencing bridge employment behavior among older adults on social media platforms. This analysis collected 757 older adults from China who continue to work as anchors in social media after retiring. Data collection was conducted over ten days via structured questionnaires divided into eight sections. Furthermore, this study conducts structural equation modeling (SEM) to process the data. The results indicate that social capital (beta = 0.183, p = 0.004) and bridge employment policies (beta = 0.123, p = 0.031) have a significant positive impact on intention to bridge employment. Subjective norms (beta = 0.197, p < 0.001), attitudes (beta = 0.204, p < 0.001), and perceived behavioral control (beta= 0.147, p = 0.004) also positively and significantly influence intention to bridge employment. Subjective norms, attitudes, and perceived behavioral control serve as crucial mediators in the relationship between social capital, bridge employment policies, and intention to bridge employment. Finally, intention (beta = 0.480, p = 0.001) is a strong predictor of bridge employment behavior and acts as a mediator within the model. The findings suggest that enhancing social capital and well-structured employment policies can significantly influence older adults’ acceptance and sustained participation in bridge employment on social media platforms.
- Keywords
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JEL Classification (Paper profile tab)J14, J22, J26, C31, L86
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References36
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Tables9
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Figures2
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- Figure 1. Empirical framework
- Figure 2. SmartPLS output of SEM
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- Table 1. Demographics
- Table 2. Reliability statistics
- Table 3. KMO and Bartlett’s test
- Table 4. Convergence validity
- Table 5. Discriminant validity test
- Table 6. Reliability statistics
- Table 7. Structural equation model
- Table 8. Bootstrap test of mediation effect
- Table A1. Measurement scale
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Conceptualization
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Data curation
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Formal Analysis
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Funding acquisition
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Investigation
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Methodology
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Project administration
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Resources
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Software
Lingzhi Liu, Songyu Jiang
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Supervision
Lingzhi Liu, Jirawan Deeprasert
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Validation
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Visualization
Lingzhi Liu, Songyu Jiang
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Writing – original draft
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Writing – review & editing
Lingzhi Liu, Jirawan Deeprasert, Songyu Jiang
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Conceptualization
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Acknowledgment
This work is supported by Tomas Bata University in Zlin through; IGA/FaME/2019/002: The role of the institutional environment in fostering entrepreneurship, and further supported by IGA/FaME/2020/002 and IGA/FaME/2019/008. The authors are thankful to Prof. Jaroslav Belás, Prof. Boris Popesko, and Prof. Miloslava Chovancová for their guidance towards developing this manuscript.