Exploring Technology Acceptance and Planned Behaviour by the Adoption of Predictive HR Analytics During Recruitment

This research aims to investigate the technology acceptance and use behaviour of hiring mangers when it comes to the adoption of predictive human resources analytics during recruitment. Additionally, this paper discusses the identification of dishonest behaviour to increase the job offer success during algorithm-based data screening. In the age of digital transformation, researchers and practitioners explore the possibilities of predictive analytics in human resource recruitment. Predictive data modelling enables hiring managers to discover attrition, reduce cognitive bias, and identify the compatibility between job candidates and organizational environments. The unified theory of technology acceptance and usage (UTAUT) will be used to identify the intention and use behaviour of hiring managers when it comes to the application of predictive HR analytics. It will also be explored how the actual system use impacts key recruitment performance indicators. The structural relationships of the UTAUT model will be examined by an empirical questionnaire and a partial least square structural equation model (PLS-SEM). To predict the misrepresentation and dishonesty practised by job candidates during algorithm-based data screening, the theory of planned behaviour is applied in conjunction with semi-structured interviews. This research uncovers to what degree human resource managers trust, accept, and integrate predictive HR analytics in daily routine. Further, data modellers and researchers should be able to test, improve, and optimize future machine-learning algorithms based on the dishonest behavioural themes identified in this research study. Finally, this research will show how software process improvement (SPI) initiatives can be constantly improved by machine learning algorithms and user group requirements.