Authors:
Emiel Caron
1
and
Saša Batistic
2
Affiliations:
1
School of Economics and Management, Tilburg University, Tilburg and The Netherlands
;
2
School of Social and Behavioral Sciences, Tilburg University, Tilburg and The Netherlands
Keyword(s):
Human Resource Analytics, Human Resource DSS, Strategic Competence Analytics, Knowledge Hubs, Data Integration, Network Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
There is a lack of consensus on the usefulness of Human Resource (HR) analytics to achieve better business results. The authors suggest this is due to lack of empirical evidence demonstrating how the use of data in the HR field makes a positive impact on performance, due to the detachment of the HR function from accessible data, and due to the typically poor IT infrastructure in place. We provide an in-depth case study of Strategic Competence analytics, as an important part of HR analytics, in a large multinational company, labelled ABC, which potentially shows two important contributions. First, we contribute to HR analytics literature by providing a data-driven competency model to improve the recruitment and selection process. This is used by the organization to search more effectively for talents in their knowledge networks. Second, we further develop a model for data-driven competence analytics, thus also contributing to the information systems literature, in developing specializ
ed analytics for HR, and by finding appropriate forms of computerized network analysis for identifying and analysing knowledge hubs. Overall, our approach, shows how internal and external data triangulation and better IT integration makes a difference for the recruitment and selection process. We conclude by discussing our model’s implications for future research and practical implications.
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