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Resulting from the rapid technological advancement in the field of artificial intelligence and its implementation in the business world, intelligent systems are gradually adopted in recruitment. As this development is fast evolving and recent, there is comparatively little research about artificial intelligence in conjunction with recruitment. Hence, this thesis aims at exploring the effects of intelligent algorithms on the recruitment process and the biases involved.To investigate the topic, existing literature was analysed and primary research in form of expert interviews was conducted.The thesis describes the current state of implementation, effects on recruiters and bias as well as potential drawbacks. Overall, it was identified that artificial intelligence cannot prevent bias in personnel selection.The findings imply the need to further research the topic, particularly the implications of algorithmic bias.
The employee referral program and the relation of extrinsic and intrinsic motivation to referrals
(2018)
The aim of this bachelor thesis is to give an overview of current practices in employee referral programs and to establish the link between motivation and propensity to refer on the basis of motivational theories in order to identify which methods a company can use to obtain the highest possible quality referrals from its employees. On the basis of relevant scientific literature, the employee referral program is described, related rewards explained and relevant motivational theories outlined. The factors of intrinsic, prosocial and extrinsic motivation, as well as the overjustification effect are evaluated, to understand their impact on an employee’s propensity to refer and to draw conclusions for practical implications.
Employers must have the necessary tools to engage in the fight for talent, which is growing increasingly competitive. The rising competitiveness of the recruiting industry today has further driven the development of the recruitment process, resulting in the introduction of artificial intelligence (AI) techniques.
In this thesis, a literature review of current applications of AI in recruitment is conducted to better understand AI’s present strengths and limitations as well as its future potential.
In particular, this thesis attempts to clarify, from a recruitment strategy perspective, how AI can be used to improve recruitment and facilitate recruiters’ daily work, with a focus on which guidelines should be in place to achieve these goals.
The results reveal a significant gap between the promise and current reality of AI applications in human resources. However, with a few adjustments and cautious implementation, AI can indeed provide recruiters with promising solutions primarily by taking over tasks such as sourcing, screening and possibly even interviewing applicants through video screening. This has the potential to improve the quality of hiring and eliminate bias in recruitment. The thesis also finds that, at present, a fully automated process without any supervision from recruiters is unrealistic, at least in the final stages of the decision-making process, due to the ongoing and crucial need for a human touch and the currently foreseen negative cultural reaction to AI in its present limited form.