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Association rule mining for job seekers' profiles based on personality traits and Facebook usage
School of Business, JAMK University of Applied Sciences, Rajakatu 35, 40100 Jyväskylä, Finland.
Jyväskylä School of Business and Economics, University of Jyväskyla, Finland.
School of Computing, University of Eastern Finland, FI-70211 Kuopio, Finland.
Department of Mathematics Statistics and Computer Science, University of Agriculture, Makurdi, Nigeria.
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2022 (English)In: International Journal of Business Information Systems, ISSN 1746-0972, E-ISSN 1746-0980, Vol. 40, no 3, p. 299-326Article in journal (Refereed) Published
Abstract [en]

Personality traits play a significant role in many organisational parameters, such as job satisfaction, performance, employability, and leadership for employers. One of the major social networks, the unemployed derives satisfaction from is Facebook. The focus of this article is to introduce association rule mining and demonstrate how it may be applied by employers to unravel the characteristic profiles of the unemployed Facebook users in the recruitment process by employers, for example, recruitment of public relations officers, marketers, and advertisers. Data for this study comprised 3,000 unemployed Facebook users in Nigeria. This study employs association rule mining for mining hidden but interesting and unusual relationships among unemployed Facebook users. The fundamental finding of this study is that employers of labour can adopt association rule mining to unravel job relevant attributes suitable for specific organisational tasks by examining Facebook activities of potential employees. Other managerial and theoretical implications are discussed.

Place, publisher, year, edition, pages
InderScience Publishers, 2022. Vol. 40, no 3, p. 299-326
Keywords [en]
association rule mining, Facebook, unemployment, personality traits
National Category
Information Systems, Social aspects
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-93648DOI: 10.1504/ijbis.2022.124933Scopus ID: 2-s2.0-85138082641OAI: oai:DiVA.org:ltu-93648DiVA, id: diva2:1704808
Note

Validerad;2022;Nivå 1;2022-10-19 (hanlid)

Available from: 2022-10-19 Created: 2022-10-19 Last updated: 2022-10-19Bibliographically approved

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Oyelere, Solomon Sunday

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
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  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
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Output format
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  • asciidoc
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