Presence of Corporate Reputation Cues in Company Vacancy Texts Boosts Vacancy Attractiveness as Perceived by Employees

R.E. Loke-, Felicia Betten

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Attracting the best candidates online for job vacancies has become a challenging task for companies. One thing that could influence the attractiveness of organisations for employees is their reputation that is an essential component of marketing research and plays a crucial role in customer and employee acquisition and retention. Prior research has shown the importance for companies to improve their corporate reputation (CR) for its effect on attracting the best candidates for job vacancies. Company ratings and vacancy advertisements are nowadays a massive, rich valued, online data source for forming opinions regarding corporations. This study focuses on the effect of CR cues that are present in the description of online vacancies on vacancy attractiveness. Our findings show that departments that are responsible for writing vacancy descriptions are recommended to include the CR themes citizenship, leadership, innovation, and governance and to exclude performance. This will increase vacancies’ attractiveness which helps prevent labour shortage.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Data Science, Technology and Applications, DATA 2024
EditorsElhadj Benkhelifa, Alfredo Cuzzocrea, Oleg Gusikhin, Slimane Hammoudi
Pages593-604
DOIs
Publication statusPublished - 2024
Event13th International Conference on Data Science, Technology and Applications - Dijon, France
Duration: 9 Jul 202411 Jul 2024

Conference

Conference13th International Conference on Data Science, Technology and Applications
Abbreviated titleData 2024
Country/TerritoryFrance
CityDijon
Period9/07/2411/07/24

Funding

This paper has been inspired on the MSc project of Felicia Betten who was involved via the master Digital Driven Business at HvA. Thanks go to D. Hagen as well as several anonymous reviewers for providing some useful suggestions to an initial version of this manuscript. Rob Loke is assistant professor data science at CMIHvA.

FundersFunder number
CMIHvA

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