GRA 2269 Leading In Organizations Using Intelligent Decision Support Systems

GRA 2269 Leading In Organizations Using Intelligent Decision Support Systems

Course code: 
GRA 2269
Department: 
Leadership and Organizational Behaviour
Credits: 
6
Course coordinator: 
Mathias Hansson
Dominique Kost
Course name in Norwegian: 
Leading in Organizations Using Intelligent Decision Support Systems
Product category: 
Master
Portfolio: 
MSc in Leadership and Organisational Psychology
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

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.

Learning outcomes - Knowledge

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).
Learning outcomes - Skills

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
General Competence

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
Course content

Topics covered in the course include:

  • 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
Teaching and learning activities

The following software is required in the course:

  • Disco (free license)
Software tools
Software defined under the section "Teaching and learning activities".
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.

Qualifications

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.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 22691
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
100No48 Hour(s)Individual 48 hours home exam
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:No
Grouping (size):Individual
Duration:48 Hour(s)
Comment:48 hours home exam
Exam code:GRA 22691
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
24 Hour(s)
Student's own work with learning resources
136 Hour(s)
Sum workload: 
160

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.