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2017/2018  BA-BSSIO2005U  Collective Intelligence: Crowdsourcing for Firm Innovation and Predictions

English Title
Collective Intelligence: Crowdsourcing for Firm Innovation and Predictions

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Bachelor
Duration One Quarter
Start time of the course Third Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc in Service Management
Course coordinator
  • Carina Antonia Hallin - EGB
Main academic disciplines
  • Information technology
  • Innovation
  • Strategy
Last updated on 30-06-2017

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: To be awarded the highest mark (12) at the written exam, the student must demonstrate fulfillment of the following learning objectives:
  • The student should be able to define a real crowdsourcing, crowdfunding and/or prediction sourcing challenge in collaboration with an external partner.
  • The student should be able to relate the challenge to relevant theories.
  • The student should be able to discuss the strengths and weaknesses in those theories.
  • The student should be able to define and test propositions.
  • The student should be able to solve a real crowdsourcing, crowdfunding and/or prediction sourcing challenge.
  • The student should be able to analyze and present the results of the analysis.
  • The student should be able to use sound arguments to support findings and draw convincing conclusions based on evidence from the analysis.
Course prerequisites
English language skills equal to B2 level (CEFR) and math skill equal to Danish level B are recommended.
Collective Intelligence: Crowdsourcing for Firm Innovation and Predictions:
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 2-3
Size of written product Max. 20 pages
The project size should be:
1 student: max 10 standard pages
2 students: max 15 standard pages
3 students: max 20 standard pages

The project can be written individually

The assignment must be written in groups of 1-3 members, but is graded individually. It must therefore be clearly identified in the paper which student is responsible for which parts of the paper.
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Spring
Make-up exam/re-exam
Same examination form as the ordinary exam
Make up exam:
1. Students that did not hand in the project or students that handed in a blanc project for the ordinary exam: A project based on the ordinary data material is handed in.

2) Re-exam (students that have failed the ordinary exam). The student can chose between two options:
a. If the data material is considered acceptable: The project must be revised building upon the existing data material from the ordinary exam.
b. If the data material is NOT considered acceptable: a new project must be written that is based on a special re-exam data-material.

It is the student's own responsibility to seek advice from the examiner regarding the type of re-exam the student should choose after the evaluation of the ordinary exam is announced.
Description of the exam procedure

The exam is a written home-assignment and a case-based project. 


The product should result in a junior-consultancy report written for an external public or private sector business partner with key emphasis on problem solving. The students will apply prediction sourcing and crowdsourcing tools learned in the course to a real case challenge. 


The consulting report should produce maximum 20 pages (3 students), 15 pages (2 students) and 10 pages (1 student), including  references. The exam must be written in groups of 1-3 members, but is graded individually. In addition to the maximum number of pages, each students must submit ½- 1 page where the student clarify and describe which parts of the report they have contributed to. They must be submitted as a part of the project after the list of contents, but they are not included in the pages for the report itself. By undersigning the Declaration of authorship, each student declares that they have read and consented to the declaration of contribution handed in by the other group members. 


The declaration of contribution is not included in the maximum number of pages that you have to state at the Declaration of Authorship.


The case exam assignment is given to the students on the first course day and should be handed in two weeks after the course has been accomplished. 


Students will be offered project supervision during the course.

Course content and structure

The study of Collective Intelligence; Crowdsourcing for Firm Innovation and Predictions is essentially the study of collective intelligence and ‘bottom-up’ information aggregation from the organization's important stakeholders (both internal and external "global" crowds) for advancing firm creativity, innovation and predictions of uncertainties.


The course builds students’ ability to set up and run ongoing crowdsourcing activities with the purpose of aggregating and using knowledge and collective intelligence from the firms’ important stakeholder groups such as employees, customers and suppliers for use in effective strategic decision making and innovation management. That is, the course builds students’ ability to analyze, select and develop innovation strategies by introducing  'crowdsourcing of innovation',  'prediction markets', 'prediction without markets' as emergent business information aggregation tools to assess changes in the firm’s internal and external environments.


The course starts with the premise that business strategy is a dynamic process which is both reactive and proactive in dealing with ongoing changes and innovation processes within the firm. The course analyzes the phenomenon of collective intelligence and cover various crowdsourcing and prediction mechanisms. That is, the course presents various tools and methods to crowdsource for innovation, creativity and predictive purposes that can be used to modify, adapt, and change new service designs and other business initiatives that can positively affect the firm’s strategic outcomes.


Crowdsourcing for firm innovation highlights the role of users and “crowds” as an important external source of innovation and will include various open innovation and crowdsourcing strategies including search mechanisms, motivational aspects, platform assessment and crowdsourcing methods. The course will also include hand-on workshops on how crowds can be used for strategic funding decisions through crowdfunding.


Crowdsourcing of predictions includes 'prediction markets', 'wisdom of crowds' and 'crowd predictions without markets' that involve the assessment of uncertainties in environmental and operational conditions. We will cover voting dynamics; risk management; strategic issue management; prediction of promising projects and in forecasting of performance metrics, product success and scenarios, natural disasters, terror and highly uncerrain (fuzzy) events such as changes in customer, employee satisfaction and brand reputation.

Teaching methods
The teaching sessions will normally be divided between lectures and class discussion. The sessions have been designed to facilitate as much active class participation as possible drawing on group activities, classroom clickers, students' case presentations and plenum discussions.
Feedback during the teaching period
Feedback to student will be provided in form of mid-way supervisions of their course projects.
Student workload
lectures 33 hours
preparation, exam 173 hours
Expected literature

There is not a single text for the course. Instead, the lectures will be based on material from updated and published papers, downloadable from CBS Library databases that will be made available on Learn.

Last updated on 30-06-2017