Construction Industry Job Image Analysis Among Job-Seekers Based on Social Media Perspective

Angela Palaco () and Xing Su ()
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Angela Palaco: Zhejiang University
Xing Su: Zhejiang University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 1710-1722 from Springer

Abstract: Abstract Construction is considered one of the world’s oldest industries. On the other hand, the emergence of new technologies has led to a significant change in job popularity among the younger generation. With COVID-19 affecting the world, many new phenomena were triggered, including the job image of the Construction Industry, causing direct consequences on the sector, such as the reduction of available labour. This study analyzes how social media data affected the construction industry’s image during the pandemic. Comments were collected on Twitter for 3 years from 2019 to 2021, followed by NLP (Natural Language Processing) methods to process the data through sentiment analysis. Specifically, this research provides insight into what motivates the younger generation to join the construction industry. The outcome can also assist construction companies in improving their incentives among the suggested dimensions and sectors to enhance the recruiting rates among young job seekers. In addition, it also provides a deep understanding of how the pandemic changed the generation’s perception of such a traditional sector. The study discovered the topics that were most frequently discussed during the peak of the pandemic, as well as how they affected construction companies. Construction jobs and work environment can be designated as one of the peak topics during this time, as well as Leadership and Management, implying that they may be a leading cause of employee turnover. The findings can directly help company behavioral management decision-makers develop and evaluate initiatives to improve construction companies’ job image.

Keywords: Construction Industry; Construction Companies; Social Media; Job Image; NLP; Topic Modelling; COVID-19 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_133

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