Crowd predictions from the frontline: A proactive decision tool for dynamic, strategic management


 

Abstract:

The project is a result of the Danish Industry Foundation’s special call for new management principles which ended earlier this year. Principal investigators are Assistant Professor Carina Antonia Hallin and Professor Torben Juul Andersen, both from the Department of International Economics and Management at CBS. The Danish Industry Foundation is partner in the project and will inform its members of the project.

As a manager in a global corporation, imagine that you within one week can collect data and receive a comprehensive report which summarizes what hundreds of your employees in global subsidiaries predict is going to benefit your company over time. And imagine, as an employee working in a local subsidiary, that top management of the global corporation makes decisions on the basis of your weekly predictions.

This scenario is made possible due to a newly developed method which makes use of prediction software. In one day, the software can analyze great amounts of data from employees (the crowd) which instantly are made available to management. The predictions of today are usually based on historical data e.g. monthly, quarterly or annual reports.

The new method is developed by Assistant Professor at CBS Carina Antonia Hallin. She is head of the research environment called Collective Intelligence Unit (CIU) and has together with her colleague Professor Torben Juul Andersen recently received a grant to test the method:

The project will establish a deeper understanding of the potential strategic value of collective intelligence, where predictions from employees on the companies’ frontline create better and more exact information for important decisions at headquarters. The project is going to test and validate the method together with Danish manufacturing companies operating in global markets.

Type:

Private (National)

Funder:

Industriens Fond

Status:

Current

Start Date:

01-07-2017

End Date:

31-08-2020

The page was last edited by: Dean's Office of Research