Transformational changes in the workforce and workplace, driven by demographics, alternative work arrangements and various distance-busting technologies, are rewriting the rules of engagement. While digital tools accelerate this shift, they also offer new ways for HR teams to attract talent, motivate and equip them. While there are already several HRM software tools in the market, none provide integrated advice for a holistic HRM approach. EBHR AS, aims to combine evidence-based psychometrics with artificial intelligence and machine learning to create EBBER - an easy to deploy and scalable cloud-based HRM platform. If successfully launched, EBBER will be the first HRM software solution in the market integrating available research and data on evidence-based HRM work and organizational psychology into a single platform. With EBBER, HR managers will be able to take informed actions on: recruiting and selection processes; working environment; automatic risk identification for individual employees and organizational units; leader evaluation with risk identification; and strategic actions that improve organizational output, which are generated automatically by EBBER. EBBER thus supports optimization of human capital, well-being, business productivity and profitability. EBBER´s architectural structure has 3 components: a database, an algorithmic structure and a survey system. The first beta version of the system will be ready in Q1 2020. The main technological challenges which need to be overcome are: 1) Implementing suitable analytics algorithms for the handling and analysis of multi-level and multivariate data sets to build predictions at individual and organizational level. This is a complex task as they are novel algorithms. ; 2) Handling of missing data in data analyses: data is gathered from individuals (personality and GMA) and organizational levels (MAU) separately, thus potentially causing situations where data of a given employee is on only one level.
Project leader: Thomas Hoff
Institution: EBHR AS