Developing a predictive model using Twitter dataset for recruiting job-fit candidates in higher education institutions.
Loading...
Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Organisations in a variety of industries are being confronted with challenging issues and trends
like population changes, globalisation, and high-performance expectations. Thus, in such a
competitive market, organizations have begun to pay particular attention to the recruitment and
selection process, as people are their most precious assets. Employees are the most important
part of any organisation as they offer values and perspectives. Employees are generally
products of universities and colleges. The growth of any university depends on its ability to
recruit and select qualified employees in terms of skills, knowledge, behaviour, and attitudes
at all levels. However, the key aspects involved in the staff selection process, have not been
thoroughly investigated. The selection process that includes interview sessions has not attracted
much research. It is argued that a job interview may fail to provide a true picture of the
suitability of a job candidate. As some candidates use deceptive ingratiation by claiming to
correspond to the interviewers and/or organization’s values, beliefs, opinions, or attitudes to
appear more appealing or pleasant, thus misleading the interviewers into selecting them for the
jobs. Interview faking appears to be hard to detect, and methods for reducing it are hardly
available. Nevertheless, it is important to assess the attitudinal suitability of potential
academics. This can be done through various techniques such as machine learning and deep
learning using social media platforms such as Twitter. Social media is an important aspect of
people’s lives nowadays. As people increase their digital presence on social networking sites,
the use of social media as a recruiting channel is slowly gaining momentum. This study aimed
at determining how the suitability of academics can be classified using Twitter dataset. To this
end, the design and development of deep learning job-fit predictive artefacts using Twitter
dataset followed rigorous steps of the design science methodology. The results of this study
reveal that academic suitability can be predicted using deep learning methods. This study
recommends that Universities, Higher education departments consider using artefacts based on
social media datasets as supplement tools to enhance the recruitment and selection process.
Description
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.