 Extracting Dimensions of Job Quality from Online Employee ReviewsCath Sleeman Discussion Papers from Economic Statistics Centre of Excellence Abstract: This paper uses a novel dataset of online employee reviews, from Indeed's UK website, to enhance our understanding of job quality. Keywords within the reviews were extracted and then clustered to form a taxonomy of job quality. It is the first UK taxonomy that is derived from employee reviews and the first to be updatable in real time. Analysis of the taxonomy shows that the emphasis placed on different dimensions of work quality has shifted over time, with a marked increase in references to culture, atmosphere and the broader workplace environment. The paper also reveals differences between occupations; workers in some occupations value pay and rewards, while others value the workplace atmosphere and environment. The dataset and methods presented here could form a useful early warning system to detect changes in the dimensions of work quality and to measure the impact of new working practices. More broadly, the paper shows the potential of a novel dataset to improve our understanding of job quality. Keywords: job quality; wellbeing; web data; machine learning; taxonomy (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eoe:escoed:escoe-dp-2024-01 Access Statistics for this paper More papers in Discussion Papers from Economic Statistics Centre of Excellence Economic Statistics Centre of Excellence, Melbourne House, Aldwych, WC2B 4PJ. Contact information at EDIRC. |
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