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2016/2017  BA-BSSIO1003U  Prediction Markets and Crowdsourcing for Firm Innovation: Service and Innovation

English Title
Prediction Markets and Crowdsourcing for Firm Innovation: Service and Innovation

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 - Department of International Economics and Management (INT)
Main academic disciplines
  • Information technology
  • Innovation
  • Strategy
Last updated on 16-08-2016
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.
Examination
Prediction Markets and Crowdsourcing for Innovation:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Group exam
Number of people in the group max. 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 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.

Re-exam (students that have failed the ordinary exam). The student can chose between two options:
1. If the data material is considered acceptable: The project, build upon the existing data material from the ordinary exam, must be revised.
2. If the data material is NOT considered acceptable: a new project must be written is based on a special re-exam data-material.
It is the students own responsibility, when the evaluation of the ordinary exam is given, to seek advice from the examiner regarding the type of re-exam the student should choose.
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 Prediction Markets and Crowdsourcingfor Firm Innovation is essentially the study of collective intelligence and ‘bottom-up’ information aggregation from the firm's important stakeholders as they sense changes in the firm’s environmental spheres for use in strategic decision making and innovation processes.

 

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 phenomena of environmental sensing by employees, suppliers and customers and presents various tools to aggregate information for innovation and predictive purposes that can be used to modify, adapt, and change new service designs and other business initiatives that affect the firm’s strategic outcomes.

 

The course will cover various prediction and crowdsourcing mechanisms.

 

Prediction markets theories and mechanisms include assessment of changes in environmental and operational conditions; voting; risk management; strategic issue management; strategic planning, prediction of promising projects and in forecasting of performance metrics., product success, natural disasters and terror and fuzzy events such as drop in customer and employee satisfaction.

 

Crowdsourcing mechanisms introduced in the course include first-, second-, third- and fourth-generation prediction aggregation; crowdsourcing of innovation and creativity, crowdsharing and open source software and crowdfunding.

 

The course builds students’ ability to set up and run ongoing crowdsourcing activities with the purpose of aggregating 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 prediction markets and prediction without markets and crowdsourcing as emergent business information aggregation tools to assess changes in the firm’s internal and external environments.

 

 


 
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.
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 16-08-2016