Digital hr and analytics
A.A. 2025/2026
Obiettivi formativi
The course "Digital HR and Analytics" aims to contribute to the MSc Management of Human Resources by equipping students with a comprehensive understanding of the intersection between Human Resources Management (HRM) and technology. This understanding will enable them to critically analyze and manage the impacts of digital technologies and HR analytics on HRM systems. By fostering a critical mindset towards technology adoption, students will develop insights into both the positive and negative implications of digital HRM, preparing them to address managerial challenges in a rapidly evolving digital landscape.
Risultati apprendimento attesi
At the end of this course, students will be able to:
Module 1 - The Digital Transformation of the HR Function:
1) Illustrate the main perspectives in the research on technology and HRM (e.g., tool, proxy, and ensemble view of technology; UTAUT).
2) Critically debate the relationship between e-HRM and the strategic role of the HRM function by adopting a resource-based view approach.
3) Identify success factors and steps in the implementation of digital HRM systems processes.
4) Distinguish between descriptive, predictive, and prescriptive HR analytics.
5) Examine workforce data and use that information to design adequate HRM initiatives.
Module 2 - AI-based HRM Functional Areas:
6) Apply the Job Characteristics Model to analyze how the adoption of AI affects job design.
7) Describe the impact of AI adoption on the main HRM practices (e.g., attraction, recruitment and selection, training and development).
8) Utilize generative AI to redesign HRM practices.
9) Critically examine the ethical implications of AI in HRM, particularly in terms of inclusion, fairness, and employee well-being.
Module 3 - Managerial Challenges in Digital HRM:
10) Identify managerial challenges in leading digital HRM initiatives, including the development of a digital mindset and effective leadership in the digital age.
11) Analyze the evolving role of leaders in leveraging AI and digital technologies to drive organizational performance and employee engagement.
Module 1 - The Digital Transformation of the HR Function:
1) Illustrate the main perspectives in the research on technology and HRM (e.g., tool, proxy, and ensemble view of technology; UTAUT).
2) Critically debate the relationship between e-HRM and the strategic role of the HRM function by adopting a resource-based view approach.
3) Identify success factors and steps in the implementation of digital HRM systems processes.
4) Distinguish between descriptive, predictive, and prescriptive HR analytics.
5) Examine workforce data and use that information to design adequate HRM initiatives.
Module 2 - AI-based HRM Functional Areas:
6) Apply the Job Characteristics Model to analyze how the adoption of AI affects job design.
7) Describe the impact of AI adoption on the main HRM practices (e.g., attraction, recruitment and selection, training and development).
8) Utilize generative AI to redesign HRM practices.
9) Critically examine the ethical implications of AI in HRM, particularly in terms of inclusion, fairness, and employee well-being.
Module 3 - Managerial Challenges in Digital HRM:
10) Identify managerial challenges in leading digital HRM initiatives, including the development of a digital mindset and effective leadership in the digital age.
11) Analyze the evolving role of leaders in leveraging AI and digital technologies to drive organizational performance and employee engagement.
Periodo: Terzo trimestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Terzo trimestre
SECS-P/10 - ORGANIZZAZIONE AZIENDALE - CFU: 9
Lezioni: 60 ore
Docente:
Lazazzara Alessandra
Turni:
Turno
Docente:
Lazazzara AlessandraDocente/i
Ricevimento:
Su appuntamento inviando una mail a [email protected]
Via Conservatorio 7 - Stanza n.7 (Ingresso da Via Mascagni, ascensore accanto al bar, salire al secondo piano, prima stanza sulla destra)