Investigating electronic word-of-mouth on social media: an eye-tracking approach
We investigate how individuals process electronic word-of-mouth (eWOM) about jobs and employers on social media. Employing eye-tracking methodology, our study investigated: (1) the cognitive processing of positive and negative eWOM hosted on social media; (2) the factors influencing job seekers'...
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sg-ntu-dr.10356-1642872023-05-19T07:31:18Z Investigating electronic word-of-mouth on social media: an eye-tracking approach Yu, Kang Yang Trevor Goh, Kim Huat Kawasaki, Shota Nanyang Business School Business::Management Credibility Employer Branding We investigate how individuals process electronic word-of-mouth (eWOM) about jobs and employers on social media. Employing eye-tracking methodology, our study investigated: (1) the cognitive processing of positive and negative eWOM hosted on social media; (2) the factors influencing job seekers' cognitive processing of eWOM; and (3) the factors influencing perceptions of the credibility of eWOM as a source of employer information. Our results suggest the following. First, job seekers devote more attention to negative compared with positive eWOM. Second, existing employer knowledge in terms of familiarity and reputation influences how positive and negative eWOM are processed. Lastly, employer reputation and job search effort enhance one's perceptions of the credibility of eWOM. Overall, this research provides insights into the cognitive processing of online employer information and its implications for job search, recruitment, and employer branding. Ministry of Education (MOE) This research/project is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 1 (RG176/17). 2023-01-13T03:48:02Z 2023-01-13T03:48:02Z 2022 Journal Article Yu, K. Y. T., Goh, K. H. & Kawasaki, S. (2022). Investigating electronic word-of-mouth on social media: an eye-tracking approach. Human Resource Management, 61(5), 599-616. https://dx.doi.org/10.1002/hrm.22099 0090-4848 https://hdl.handle.net/10356/164287 10.1002/hrm.22099 2-s2.0-85123065092 5 61 599 616 en RG176/17 Human Resource Management © 2022 Wiley Periodicals LLC. All rights reserved. |
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Business::Management Credibility Employer Branding Yu, Kang Yang Trevor Goh, Kim Huat Kawasaki, Shota Investigating electronic word-of-mouth on social media: an eye-tracking approach |
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We investigate how individuals process electronic word-of-mouth (eWOM) about jobs and employers on social media. Employing eye-tracking methodology, our study investigated: (1) the cognitive processing of positive and negative eWOM hosted on social media; (2) the factors influencing job seekers' cognitive processing of eWOM; and (3) the factors influencing perceptions of the credibility of eWOM as a source of employer information. Our results suggest the following. First, job seekers devote more attention to negative compared with positive eWOM. Second, existing employer knowledge in terms of familiarity and reputation influences how positive and negative eWOM are processed. Lastly, employer reputation and job search effort enhance one's perceptions of the credibility of eWOM. Overall, this research provides insights into the cognitive processing of online employer information and its implications for job search, recruitment, and employer branding. |
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Nanyang Business School |
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Nanyang Business School Yu, Kang Yang Trevor Goh, Kim Huat Kawasaki, Shota |
format |
Article |
author |
Yu, Kang Yang Trevor Goh, Kim Huat Kawasaki, Shota |
author_sort |
Yu, Kang Yang Trevor |
title |
Investigating electronic word-of-mouth on social media: an eye-tracking approach |
title_short |
Investigating electronic word-of-mouth on social media: an eye-tracking approach |
title_full |
Investigating electronic word-of-mouth on social media: an eye-tracking approach |
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Investigating electronic word-of-mouth on social media: an eye-tracking approach |
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Investigating electronic word-of-mouth on social media: an eye-tracking approach |
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investigating electronic word-of-mouth on social media: an eye-tracking approach |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/164287 |
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1772827634808389632 |