GRA 2271 Understanding Organizations and Leadership Through Advances in Computational Social Science

GRA 2271 Understanding Organizations and Leadership Through Advances in Computational Social Science

Course code: 
GRA 2271
Leadership and Organizational Behaviour
Course coordinator: 
Anton Gollwitzer
Course name in Norwegian: 
Understanding Organizations and Leadership Through Advances in Computational Social Science
Product category: 
MSc in Leadership and Organisational Psychology
2023 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Are you curious about how we can better understand organizations and leadership through recent methodological advancements in psychology and sociology? Are you interested in learning about novel sources and applications of psychological and organizational data (e.g., location tracking, sensors and monitors)? Do you want to have a professional edge by understanding how we can improve institutions and organizational systems by integrating recent methodological advancements in the social sciences? Welcome to the intersection of Organizational Research, Social Science, and Methodological Advancements: Understanding Organizations and Leadership Through Advances in Computational Social Science.

In this course, you will learn how we can better understand organizations, leadership, and psychological phenomena by applying the young field of Computational Social Science to these topics. In doing so, we can help solve psychological, socio-cultural, and organizational problems through novel data methods and technologies. With rapid advancements in computation, scientists and organizations are no longer beholden to basic strategies, limited data, and basic analysis tools. Recent computational advances allow us to apply new methods and techniques to understand things like institutional frameworks, organizations, leadership, and human behavior, with the ultimate aim of providing solutions to societal and organizational problems.

Please note that the course aims to provide a broad overview of how organizations, leadership, and psychology can be informed by recent methodological advances (e.g., social networks, natural language processing) rather than an in-depth study on any specific method. It is not a data science or coding skills-based course.  

I look forward to teaching you how we can better understand organizations, leadership, and psychology via advances in the field of Computational Social Science.

Prerequisite: None. Coding or data analysis experience is not required. This is an overview course of how organizations, leadership, and psychology can be informed by CSS. It is not a data science or coding skills-based course.

Learning outcomes - Knowledge
  • Overview of how organizational topics and human psychology can be informed by CSS.
  • Basic knowledge of the advancements that have been made in social and organizational sciences by applying computational techniques of CSS.
  • Creative methodological ways to solve organizational problems.
Learning outcomes - Skills
  • Skills entailing ways to examine organizational and leadership questions via new methods and approaches.
  • Skills to interpret findings in the field of CSS and how these findings inform psychological topics.
  • Skills to solve organizational problems with modern computational methods.
General Competence
  • Appreciation for and understanding of how organizations and other topics, such as human resources, can be informed by the relatively young field of CSS.
  • Critical reflection on the benefits as well as limitations of varying methods in terms of informing organizational and psychological research.
Course content

The course covers the following topics                                  

  • The structure and dynamics of social systems.
  • Social cognitive processes, including phenomena like social contagion and wisdom of crowds.
  • Understanding organizations and groups in terms of modern computational methods, including social networks, natural language processing and artificial intelligence and machine learning. 
  • Novel sources of data (e.g., geo-location, sensors) in organizations.
  • The ethics of data privacy in organizations and beyond.
Teaching and learning activities

The course is structured as a combination of lectures, discussions, in-class activities, and asynchronous/synchronous group work. The course will be held either in person or on Zoom (digital lectures) with active participation. The course will rely on research and popular articles, as well as online walkthroughs. The course assessment is a final exam—multiple choice and short answer questions.  

Software tools
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

This is a course with a structured exam assessment (a single final exam). Students who fail to participate in the exam will fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.


All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge


Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Form of assessment:
Structured test
Exam code:
GRA 22711
Grading scale:
Grading rules:
Internal examiner
Examination when next scheduled course
100Yes 2 Hour(s)
  • Bilingual dictionary
Individual .
Exam category:Submission
Form of assessment:Structured test
Weight: 100
Grouping (size):Individual
Support materials:
  • Bilingual dictionary
Duration: 2 Hour(s)
Exam code:GRA 22711
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
24 Hour(s)
Prepare for teaching
60 Hour(s)
Student's own work with learning resources
60 Hour(s)
Work with asynchronous materials
16 Hour(s)
Incl. review and preparation
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.