GRA 2269 Leading in Organizations Using Intelligent Decision Support Systems
GRA 2269 Leading in Organizations Using Intelligent Decision Support Systems
In today’s organizations, algorithms play a transformative role in decision-making, influencing workflows and employee experiences. This course equips students with the theoretical and practical tools to understand, evaluate, and innovate within the rapidly evolving field of technology-driven decision-making. Students will explore how decision support systems reshape organizational processes and employee outcomes, using a multi-level perspective that includes individual, team, and organizational contexts. At the individual level, students will analyze the impact of algorithmic decisions on employee motivation, performance, and meaningfulness of work. At the team level, they will evaluate how collaborative decision-making with algorithms affects team dynamics and outcomes. At the organizational level, students will explore the opportunities and challenges of becoming data-driven, including the integration of decision support tools in organizational decision making processes.
By the end of the course, students will have gained advanced knowledge of organizational psychology and leadership theories in the context of intelligent decision-making systems. They will develop critical skills to analyze and evaluate these systems, propose innovative frameworks for their implementation, and demonstrate competence in leading organizations through data-driven transformations.
By the end of the course the candidate:
- Understands how intelligent decision support systems challenge and extend contemporary organizational theories (e.g., decision-making, routines, team dynamics, and motivation theories).
- Critically evaluates the implications of decision support systems for employee outcomes (e.g., performance, motivation, meaningfulness) and identifies opportunities for theoretical innovation.
- Can explain the organizational challenges of integrating data-driven systems across business functions.
- Synthesizes insights from organizational psychology to propose how decision support systems can transform work processes, such as business models and routines.
By the end of the course the candidate can:
- Apply organizational theories to critically evaluate the impact of decision support systems on teams and businesses.
- Propose frameworks for leaders to address challenges in data-driven decision-making.
- Facilitate discussions on opportunities and challenges for leaders and managers related to data-driven decision-making and AI
- Identify organizational issues and challenges that can be solved by decision support systems
By the end of the course the candidate:
- Navigates and evaluates emerging technologies in decision-making to guide both leadership and organizational practices.
- Demonstrates critical reflection on the ethical and organizational implications of algorithm-driven decision-making.
- Communicates with employees, experts, and decision makers about selection and adaptation of decision support systems, as well as development of new business models.
Topics covered in the course include:
- Algorithmic Management, Digital labour and gig work: how intelligent decision support systems affect work and its outcomes (e.g., careers, meaningfulness, leadership processes, and team mental models)
- Multiagent teams: intelligent decision support systems, team cognition, and decision making
- Trust and reliability of algorithmic decision making from the perspective of organizational psychology
- Mapping and understanding business processes and routines from the perspective of organizational theory
- Platforms and technologies enabling intelligent decision support systems
- Human-AI symbiosis in organizational decision making
This is an applied course and requires active class participation. We use various teaching methods, such as guest lecturers, case exercises and analysis, and using data to analyse and interpret organizational procsses.
The following software is required in the course:
- Disco (free license)
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.
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.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Students need to be able to apply current organizational psychology theories and frameworks they have learned in their first year MSc courses, such as (but not limited to) organizational science, motivational science, judgement and decison making in organizations.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 100 Grouping: Group (2 - 4) Duration: 1 Semester(s) Comment: Term paper in groups (max. 4 students). Content of the term paper will be introduced in the first lecture. Students can work on the term paper throughout the duration of the course. Exam code: GRA 22691 Grading scale: ECTS Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 24 Hour(s) | |
Student's own work with learning resources | 136 Hour(s) |
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.
