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2023/2024  KAN-CGMAO2002U  Market Informed Decisions

English Title
Market Informed Decisions

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for cand.merc. and GMA (GMA)
Course coordinator
  • Michel Van der Borgh - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
  • Statistics and quantitative methods
  • Strategy
Teaching methods
  • Blended learning
Last updated on 12-12-2023

Relevant links

Learning objectives
At the end of the course, students will be able to manage work- and development situations which are complex, unpredictable, and require new solution models by demonstrating the achievement of the following learning objectives. More specifically, students will be able to:
  • Frame, describe, and diagnose a wicked business management challenge analytically using exploratory and explanatory research diagnostics.
  • Specify the objective and design components of the solution model by utilizing qualitative and quantitative output from exploratory and explanatory diagnostics.
  • Develop, on a scientific basis, solution directions that address the identified management challenge.
  • Describe procedures for systematically selecting a model-based intervention that is rooted in analyses of the model’s robustness, validity, and impact.
  • Follow academic conventions in their communications.
Market Informed Decisions:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Group exam
Please note the rules in the Programme Regulations about identification of individual contributions.
Number of people in the group 4-5
Size of written product Max. 35 pages
Assignment type Case based assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 7 days to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The students will need to respond to a case situation in which a business challenge is outlined and accompanied with sufficient information and data for students to engage in analytical efforts to develop and present market informed decisions. The recommendations should be well grounded in theoretical and methodological approaches covered during the course and communicated clearly to a target audience of business decision makers.

Course content, structure and pedagogical approach

Managers are confronted with wicked market challenges daily. A wicked problem has innumerable causes, is tough to describe, and doesn’t have a right answer. Environmental degradation, climate change, and inequality are classic examples of wicked problems. Managers face wicked challenges related to strategy shaping, new product diffusion, and customer engagement.


This course aims to develop students’ abilities to make informed decisions on how to deal with wicked market challenges. By combining economic theory (e.g., market structure, demand elasticities, economic performance, customer behavior, ecosystem management) with simulation techniques (e.g., agent-based modelling, system dynamics), students will develop the skills, capabilities, and knowledge to (a) identify and frame a company’s market challenge (What is the challenge?), (b) diagnose the market context (What do we know, and what not?), (c) utilize available market information to provide solutions for the challenge (How can we (re-)shape the market?), and (d) explain ways to implement and evaluate the suggested solution (What is the impact of the chosen solution?).


As such, this course provides students with the capabilities to effectively deal with complex markets, help companies develop market agility, utilize market intelligence, and, finally, improve customer success management.

Description of the teaching methods
Making informed decisions requires decision-driven analytics in which developing a holistic system-level understanding of the problematic situation facing the manger is the starting point. Throughout the course students will be introduced to different ways of conceptualizing and modelling decision situations. The foundation for the course is the real-life decision situation of specific business firms. The course gradually encompasses more elements of complexity by introducing methods and techniques that capture different facets of the situation. Students will be introduced to different economic models and simulation techniques as well as methods to empirically estimate critical parameters.
Feedback during the teaching period
Teacher and group feedback on presentations and product ideas (pitches).
Workshop-based discussions in class. In workshops students will work firsthand with a real-world business situation. The first part of the workshops focuses on identification, characterization, and validation of the field problem whereas the second part of the workshops focuses on solution model development and solution justification. Students will receive direct feedback during the workshops and subsequently through feedback on their responses to two assignments.
Student workload
Teaching 33 hours
Preparation 73 hours
Group work outside of class 50 hours
Exam 50 hours
Expected literature

A list of relevant literature will be provided in class. Below please find an indicative literature:



Wilensky, U., & Rand, W. (2015) An Introduction to Agent Based Modelling: Modelling Natural, Social and Engineered Complex Systems with NetLogo. MIT Press, Cambridge, Massachusetts


Articles and chapters from books:

Ackoff, R. L. (1994). Systems thinking and thinking systems. System Dynamics Review, 10(2‐3), 175-188.

Baldwin & Clark, K.B. (2000). The Microstructure of Designs, ch. 2 in Design Rules: The Power of Modularity. The MIT Press

Buchanan, R. (1992). Wicked Problems in Design Thinking. Design Issues, 8(2), pp. 5-21.

Checkland, P. (1994). Systems theory and management thinking. American Behavioral Scientist, 38(1), 75-91.

Chica, M., & Rand, W. (2017). Building agent-based decision support systems for word-of-mouth programs: A freemium application. Journal of Marketing Research54(5), 752-767.

Ethiraj, S. K., & Levinthal, D. (2004). Bounded rationality and the search for organizational architecture: An evolutionary perspective on the design of organizations and their evolvability. Administrative Science Quarterly, 49(3), 404-437.

Follett, M.P. (1924). Experience as Creating, ch. 9 in Creative Experience.

Follett, M.P.(1949). The process of control, ch. 6 in Freedom and Co-ordination: Lectures in Business Organization.

Lam, S. K., & Van der Borgh, M. (2021). On salesperson judgment and decision making. Journal of the Academy of Marketing Science49(5), 855-863.

Lave, C.A. & March, J.G. (1993). An Introduction to Speculation, ch. 2 in An Introduction to Models in the Social Sciences. University Press of America.

Overgoor, G., Chica, M., Rand, W., & Weishampel, A. (2019). Letting the computers take over: Using AI to solve marketing problems. California Management Review61(4), 156-185.

Roland, J.B. & Collopy, F. (2004). Design Matters for Management, ch. 1 in Managing as Designing. Stanford University Press.

Simon, H.A. (1962), “The architecture of complexity”, Proceedings of the American Philosophical Society, Vol. 102 No. 6, pp. 467-482.

Simon, H. A. (1969). The sciences of Design: Creating the Artificial, ch. 5 in The sciences of the artificial (3rd edn, 1996) MIT Press.

Sterman, J. D. (2001). System dynamics modeling: tools for learning in a complex world. California management review, 43(4), 8-25.

Sterman, J. D., Repenning, N. P., & Kofman, F. (1997). Unanticipated side effects of successful quality programs: Exploring a paradox of organizational improvement. Management science, 43(4), 503-521.

Van der Borgh, M., Xu, J., & Sikkenk, M. (2020). Identifying, analyzing, and finding solutions to the sales lead black hole: A design science approach. Industrial Marketing Management, 88, 136-151.

Van der Borgh, M., Cloodt, M., & Romme, A. G. L. (2012). Value creation by knowledge‐based ecosystems: evidence from a field study. R&D Management, 42(2), 150-169.

Last updated on 12-12-2023