Digital hr and analytics
A.A. 2024/2025
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
Programma
The Digital HR & Analytics course is structured around 3 main modules. The first module addresses the digital transformation of the HR function, illustrating how technology and data impact the work performed within the HR function and its strategic role within the company. It will present the main theoretical approaches to explain changes and challenges affecting HRM. The second module focuses on various AI-based HRM functional areas (e.g., AI-based recruitment and selection, AI-based training and development). Through the adoption of group activity based on generative AI, students will analyze the critical factors to consider when using AI to redesign HRM practices. Finally, the last module introduces the main managerial challenges in digital HRM, with a specific focus on how AI is changing and challenging leadership. Analytics concepts and methodologies will be integrated throughout the three modules to demonstrate practical examples and applications to real digital HR problems.
Prerequisiti
The following preliminary knowledge is recommended: Human Resource Management, Human Resource Information Systems, and Organizational Behavior.
Metodi didattici
The course comprises traditional lectures, small group activities, problem-based learning, case studies, and testimonials from HR professionals or managers. Additionally, in-class activities involving Gen AI will be integrated extensively into the course.
Materiale di riferimento
Required Course Materials for attending and non-attending students: Due to the fast-evolving nature of the subject taught in this course (i.e., GenAI in HRM), an updated list of references for attending students and recommended further readings for non-attending students will be provided at the beginning of the course (April 2025). However, for all those who will take the exam by April 2025, the suggested course materials for the academic year 2023-2024 remain valid.
Modalità di verifica dell’apprendimento e criteri di valutazione
For attending students, the achievement of the expected learning outcomes will be assessed through:
1) a group project involving both analytical work and the design of HR activities to address issues identified in the analysis. Each group will receive a mark ranging from 0 to 30 cum laude based on their project work.
2) a written test consisting of 15 multiple-choice questions (MCQs) and 3 open-ended questions. Regarding the MCQs, there will be only one correct answer, and one point will be awarded for each correct answer (zero points for each wrong or missing answer). For each open-ended question, the student will receive a grade from 0 to 5. The final grade of the test (sum of the grade in the MCQs test + grade of the open-ended questions) will range from 0 to 30 cum laude. The student will have 1.5 hours to complete the test.
The final mark will be the weighted average between the group project work evaluation (30%) and the individual final test (70%).
For non-attending students, the achievement of the expected learning outcomes will be assessed through a written test as described above.
1) a group project involving both analytical work and the design of HR activities to address issues identified in the analysis. Each group will receive a mark ranging from 0 to 30 cum laude based on their project work.
2) a written test consisting of 15 multiple-choice questions (MCQs) and 3 open-ended questions. Regarding the MCQs, there will be only one correct answer, and one point will be awarded for each correct answer (zero points for each wrong or missing answer). For each open-ended question, the student will receive a grade from 0 to 5. The final grade of the test (sum of the grade in the MCQs test + grade of the open-ended questions) will range from 0 to 30 cum laude. The student will have 1.5 hours to complete the test.
The final mark will be the weighted average between the group project work evaluation (30%) and the individual final test (70%).
For non-attending students, the achievement of the expected learning outcomes will be assessed through a written test as described above.
SECS-P/10 - ORGANIZZAZIONE AZIENDALE - CFU: 9
Lezioni: 60 ore
Docente:
Lazazzara Alessandra
Turni:
Turno
Docente:
Lazazzara AlessandraDocente/i
Ricevimento:
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