Informating HRM: a comparison of data querying and data mining

Authors:
Stefan Strohmeier;Franca Piazza
Affiliations:
Chair of Management Information Systems, Saarland University, Campus C3.1, 66123 Saarbruecken, Germany.;Chair of Management Information Systems, Saarland University, Campus C3.1, 66123 Saarbruecken, Germany
Venue:
International Journal of Business Information Systems
Year:
2010

Citing 13
Cited 1

In the age of the smart machine: the future of work and power

In the age of the smart machine: the future of work and power
C4.5: programs for machine learning

C4.5: programs for machine learning
Methodological and practical aspects of data mining

Information and Management
Investigating information systems with action research

Communications of the AIS
Introduction to SQL

Introduction to SQL
Data mining for human resource information systems

Data mining
Employee turnover: a neural network solution

Computers and Operations Research
An architecture for a next-generation holistic e-recruiting system

Communications of the ACM - Creating a science of games
Data mining in course management systems: Moodle case study and tutorial

Computers & Education
Data mining techniques for better decisions in human resource management systems

International Journal of Business Information Systems
Special issue on action research in information systems: making is research relevant to practice--foreword

MIS Quarterly
Managing risk in software process improvement: an action research approach

MIS Quarterly
Design principles for competence management systems: a synthesis of an action research study

MIS Quarterly

Domain driven data mining in human resource management: A review of current research

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Beyond mere automation of tasks, a major potential of Human Resource Information Systems (HRIS) is to informate Human Resource Management (HRM). Within current HRIS, the informate function is realised based on a data querying approach. Given a major innovation in data analysis subsumed under the concept of 'data mining', possibly valuable potentials to informate HRM are lost while overlooking the data mining approach. Therefore our paper aims at a conceptual evaluation of both approaches. We therefore discuss and evaluate data mining as a novel approach compared to data querying as the conventional approach to informating HRM. Based on a robust framework of informational contributions, our analysis reveals interesting potentials of data mining to generate explicative and prognostic information. Thus data mining enriches and complements the conventional querying approach. Furthermore, recommendations for future research are derived in order to deepen the knowledge on the contributions of data mining to informate HRM.