Digital HR and Analytics

A.Y. 2020/2021
Overall hours
Learning objectives
The course Digital HR and Analytics aims to contribute to the MSc Management of human resources goals by offering grounded in research and connected to practice tools to understand and manage the intersection of HRM and technology. As the importance of digital technologies in the management of people has increased, so has research on tools, processes, and theories for doing this more effectively. The focus will be on how the transformation of the HR function through technology is leading operational HR processes to become automated and data-driven and the new ways to manage workers through technology. Students will develop a thorough understanding of the impact of digital technologies on HR systems and will critically reflect upon the new role of the HR function in handling the managerial challenges that such transformation poses. Moreover, since the impact of digital technologies on HR systems has increased human connectivity and thereby transformed the dynamics of social networks, the capability of conducting Social Network Analysis (SNA) is becoming a critical competence of HRM for efficiently detecting critical connections and understanding the informal hierarchies within an organization. Therefore, another main objective of the course is to familiarize the students with the theory, methodology and research associated with SNA in organizational contexts. Students will develop an in-depth understanding of how HR analytics function can use SNA to help improve operational effectiveness.
Expected learning outcomes
At the end of this course, students should be able to:
1. Explain the fundamental concepts of digital HR
2. Describe the latest developments and challenges in the fields of digitalisation of HRM
3. Discuss the main theories useful to explain technology adoption and its effects in the HR domain
4. Consult and make a critical appraisal of the scientific literature on the major issues related to digital HR
5. Identify and understand scientific evidence on success factors for the development and implementation of digital HR systems
6. Apply knowledge to develop HR solutions in order to face managerial challenges in digital HR
7. Understand the basic notations and terminology used in network science
8. Discuss the main theories that underly the social network perspective
9. Visualize, summarize, and compare networks
10. Collect, assemble, and analyze network data
11. Perform basic network analysis in R programming language
Course syllabus and organization

Single session

Lesson period
Third trimester
Teaching methods
The lessons will be held on the Microsoft Teams platform and can be followed both synchronously based on the timetable of the third quarter and asynchronously as they will be recorded and uploaded on that same platform.

Course syllabus and Teaching Resources
The contents and reference material will not be changed.
Exams will be oral through Microsoft Teams or whenever general norms and sanitary circumstances will permit it, they will be written as planned in the syllabus
Course syllabus
During the Digital HR module, the following topics will be analyzed: a) how technology affects work that is performed within the HR function and its role within the company b) main theoretical approaches to explain changes and challenges affecting digital HR c) success factors for designing and managing main functional areas (e.g., e-recruitment, e-selection, e-learning, e-performance management d) managerial challenges in digital HR (e.g., e-leadership, managing virtual teams, dark side).

During the Analytic module, the following topics will be analyzed: a) the relevance of SNA in contemporary social science research, and particularly, in organization science research; b) basic concepts in SNA and graph theory; c) social network methods and data collection; d) the strength of weak ties vs. strong ties perspective of social capital; e) intra-organizational social/relational capital and its impact on individual performance; f) advanced centrality measures (closeness and betweenness centrality) and structural holes; g) antecedents of network variables.
Prerequisites for admission
Preliminary knowledge of Human Resource management and Human Resources Information Systems is recommended.
Teaching methods
Module Digital HR (40h): The course is held through traditional lectures, small group activities, problem-based learning, case studies, and HR professionals and managers company testimonials under the guidance of the teacher.
The educational activities are supported by the Ariel platform of the University of Milan (

Module Analytics (20h): During the course, a number of selected academic articles will be reviewed and discussed in the classes. From a technical point of view, the students will familiarize with the standard tools applied in SNA by using the R software package.
Teaching Resources
A list of references and recommended further reading to enable the students to gain a deeper awareness and understanding of each topic will be provided at the beginning of the course
Assessment methods and Criteria
Module Digital HR: The exam will be based on a written test with open questions and the analysis of a short case study.

Module Analytics:
1) On-class discussions and exercises (10%)
2) Teamwork project where students should collect, analyze and interpret network data (30%)
3) The Final Exam will be based on a written test with both multiple and open questions (60%)

*For 2nd year MLS students, the HR Metrics module (20h) of Personnel Economics & HR Metrics course refers to the program of module "Analytics".

**The final mark will be the weighted average between the Digital HR exam (2/3) and the Analytics exam (1/3).
Lessons: 60 hours