Developing a predictive model using Twitter dataset for recruiting job-fit candidates in higher education institutions.
Vela, Junior Vela.
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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.