Thiscourse is structured as a lecture and a seminar. The lecture focuses on gainingan understanding of the fundamentals of human resource management andorganizational design. The lecture also introduces quantitative and businessanalytics methods for decision making in the field. In the lecture, the basictheoretical concepts are explained and discussed, whereas they are applied throughthe preparation of a seminar thesis in the seminar.
Organizational Design &Human Resource Management
The processes of developing organizational structures for small and mid-sized corporations as well as for large multinational enterprises;
The adaptation of organizations and their structures to the competitive environment, with special focus on international operating organizations and global markets;
Introduction to human resource management from a strategic and international perspective (incl. the typical challenges of international organizations);
Key elements of human resource management (incl. design of work, employee recruitment, development, separation & retention);
Introduction of methods and models for decision making in organizational design and human resource management.
Possible Applications of the Theoretical Concepts
Big data in organizations and human resource analytics;
Business analytics and machine learning methods (e.g., factoranalysis, regression analysis, and structural equation modeling);
Models for the management of organizations and human resourcemanagement (e.g., job satisfaction and turnover intention, motivation andorganizational commitment).
Leistungsnachweis:
m1733-2021 - Foundations in Organizational Design and Human Resource Management<ul><li>p1686-2021 - Foundations in Organizational Design and Human Resource Management: Klausur schriftlich</li></ul><br>m1733-2022 - Foundations in Organizational Design and Human Resource Management<ul><li>p1686-2022 - Foundations in Organizational Design and Human Resource Management: Subject theoretical and practical work</li></ul>
ECTS-Kreditpunkte:
3
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.
Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.
Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.
Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.
2020
Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.
Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.