GRA 6232 Management Control
GRA 6232 Management Control
The management accounting or controller role has undergone substantial change. The former “bean counter” has been complemented by a business partnering role, translating financial and non financial information into managerial decision support at senior and divisional levels. Contemporary organisations are increasingly flexible and decentralised, with stronger strategic, service, and technology orientations. Management control therefore requires a multidisciplinary toolkit that connects strategy, organisation design, performance measurement, and digital platforms.
This course develops the knowledge and skills required for a Controller or CFO role. The course is organised around three streams: Theory, Tools, Tradecraft. It is structured in four thematic clusters that build cumulatively across the course.
After completing the course, students should be able to:
- Explain and compare central management control perspectives and frameworks, and show how they relate to strategy, governance, and organisation design.
- Demonstrate advanced knowledge of management control as a package, including how controls interact and create intended and unintended behavioural effects.
- Demonstrate thorough knowledge of relevant scholarly theories and methods used in management control research and analysis, including strengths, limitations, and assumptions.
- Analyse how digitalisation and AI change the design, use, and legitimacy of control practices, including accountability, transparency, and communication to stakeholders.
After completing the course, students should be able to:
- Diagnose a management control problem in a case organisation by critically analysing sources and evidence, and by structuring a coherent scholarly argument.
- Evaluate alternative control system designs using explicit criteria. Example strategic fit. behavioural consequences. implementability. and ethical risk. Then justify a recommended design.
- Apply relevant analytical methods independently to a defined control problem, and produce defensible conclusions supported by evidence and appropriate theory.
- Design, test, and iterate one or more course relevant AI Agents (Gems) that support management control analysis and communication, and document agent logic, boundaries, source basis, limitations, and prompt log so that the AI Agent (Gem) is auditable.
- Produce an independent, limited research or development project under supervision, including problem formulation, method choice, and research ethics compliance.
After completing the course, students should be able to:
- Communicate extensive independent work using correct terminology and appropriate visualisations, for both specialist audiences and non specialists.
- Identify and analyse relevant academic, professional, and research ethical issues in management control, including risks created by AI supported analysis and reporting.
- Apply knowledge and skills in new contexts to complete advanced assignments and projects, including proposing implementable control changes.
- Contribute to new thinking and innovation processes by translating control insights into practical recommendations, including where AI should, and should not, be embedded in control routines.
Topics covered include:
- Management control as a package of controls.
- Strategy, governance, and stakeholder environments.
- Organisation design as a control vehicle, including team-based and process-based structures.
- Budgeting, forecasting, and contemporary alternatives.
- Responsibility centres and behavioural consequences of control.
- Performance measurement and performance communication, including non-financial metrics and sustainability relevant information.
- Implementation of control changes, including governance, behavioural risks, and learning loops.
- Digital platforms and AI as interfaces for analysis, reporting, communication, and collaboration in the CFO or Controller role.
Topics are organised in four clusters:
- Strategy and Governance (public sector).
- Organisation Design and Governance (not-for-profit sector).
- Performance and Communication.
- Technology Platforms, Boundaries, and Design.
The course follows a flipped classroom model and is organised into four clusters of three sessions each, aligned with the three streams of Theory – Tools -Tradecraft. Theory sessions provide conceptual framing and vocabulary. Tool sessions develop the analytical toolkit and method use. Tradecraft sessions emphasise professional judgement through case-based work and practice-oriented perspectives.
AI is an explicit part of the learning design and is treated as a method and work practice that must be transparent and auditable. Google Gemini is used for structured reasoning and for designing AI Agents (Gems) that operationalise parts of the course toolkit. NotebookLM is used for literature and document analysis, structured note-taking, source-grounded synthesis, and drafting support. In class and in course work, students learn to move from exploration to verification and application, and to treat AI output as a starting point for analysis rather than an authoritative answer.
The learning design is cumulative across the two assessed components. Each case is a standalone application task within its thematic cluster, using the same three step deliverable structure to build consistent competence in diagnosis, design, and AI enabled work practice. The course paper develops this further through staged research writing. moving from problem domain, to research gap, to research aim and research question.
Assessment
The course has two assessed components. One linked to case based work (40% weight) and one linked to the course paper (60% weight). Both components include assessed AI Agent (Gem) deliverables and documentation.
Case component. For each of four thematic clusters, students work on a case. The deliverable follows a standard three step structure of problem diagnosis, implementation, and AI Agent (Gem) support. Assessment applies three rubrics: Preparation, Presentation, Prompts. Using a standard three-step structure across all four cases ensures consistent scaffolding, comparable assessment, and clear progression from analysis to design to AI supported operationalisation.
Course paper component. Students conduct independent research work in small groups as a warm up for the Master thesis and to practise the academic research & writing process. The work is staged, moving from problem domain, to research gap, to research aim, research question, and paper structure. Students use NotebookLM as a source-grounded workflow for the paper development Students also develop one AI Agent (Gem) to support the research workflow. Assessment applies the same three rubrics of Preparation, Presentation, Prompts.
Detailed rubric descriptors and examples are provided in the course outline.
Computer based tools
Students will work with:
- Google Gemini, including creating, testing, and iterating AI Agents (Gems) for casework and paper workstreams.
- Google NotebookLM, including building structured notebooks, producing source grounded notes, and supporting synthesis and writing.
- Standard spreadsheet and presentation tools for quantitative work and communication when relevant.
AI use is permitted and expected, but it must be transparent and auditable. Outputs must reflect student judgement and synthesis. Students must disclose how AI Agents (Gems) and NotebookLM were used, what sources were used, what was excluded, and where human judgement overrode or corrected AI output. Prompt logs and AI Agent (Gem) documentation are part of the mandatory submissions for transparency in assessment and are evaluated under the Prompts rubric.
Further specifics about workload, reading list, deliverables, grading criteria, and rubric descriptors are described in the course outline.
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.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission other than PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Individual Duration: 1 Semester(s) Comment: Cases (included in one submission) Exam code: GRA 62322 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 60 Grouping: Group (2 - 3) Duration: 1 Semester(s) Comment: Term paper. Group composition will be based on student self-selection. Further specifics on the what and how of the term paper will be included in the course outline/syllabus. Exam code: GRA 62323 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Student's own work with learning resources | 124 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.
