GRA 2269 Leading in Organizations Using Intelligent Decision Support Systems
GRA 2269 Leading in Organizations Using Intelligent Decision Support Systems
In contemporary organizations, employees and managers both make decisions with and are affected by decisions made by algorithms. This transforms organizations and workflows. In this course, we aim to show how leaders and organizations can utilize technology and data to make better decisions, and how this can affect organizational processes and employee outcomes. This course uses current organizational and decision-making theories to understand how decision support tools can help organizations become better organized for decision making and task solving. As future leaders and practitioners within the field of leadership and organizational psychology, it is important for students to learn and understand how technology and decision support systems affect leadership and team processes as well as employee outcomes. The goal is to explore this topic from a multi-level perspective including individual, team, and organizational side. At the individual level, this course explores how decisions made by algorithms affect work and employee outcomes, such as meaningfulness and motivation. At the team level, we will ask how teams make collaborative decisions with algorithms and how this affects team interactions, processes, and outcomes. At the organizational level, we will explore how organizations and leaders can become more data driven and utilize the new technology to create value and change organizational processes.
By the end of the course the candidate:
- has advanced knowledge of how intelligent decision support systems challenge our current understanding of contemporary theories within organizational psychology and theory (e.g., decision-making, routines, technology, team, and motivational theories)
- has advanced knowledge of how decision support systems affect individual and team outcomes, such as performance and work meaningfulness
- has advanced knowledge of how decision support systems affect team and leadership processes in organizations (such as team/shared cognition)
- has generic knowledge of decision support systems and of some of the most the technologies disrupting the organizational assumptions
- has advanced knowledge of opportunities for increased value creation, but also technical and organizational challenges, which lie in combining, integrating, and presenting data from different parts of the business
- has advanced knowledge of how technology can change work processes (e.g., organizational routines, business models and influence decision-making).
By the end of the course the candidate:
- can discuss opportunities and challenges for leaders and managers related to data-driven decision-making and AI
- can apply relevant organizational theories and empirical data to analyze, critically discuss, and evaluate decision models, and intelligent decision support systems
- can apply theoretical models within the field of organizational psychology to understand the consequences of digital influence on businesses, team and employee outcomes, and critically evaluate them
By the end of the course the candidate:
- can navigate in the landscape of technologies needed to enable data-driven capabilities and how leaders and organizations can utilize such innovations
- can communicate with employees, experts, and decision makers about selection and adaptation of decision support systems, as well as development of new business models
- can stay up to date on developments in intelligent decision-making systems and 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 |
|---|
Duration type : Semester(s) Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 100 Grouping: Group (2 - 4) 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.