Name of the participant: Steffen Schnitzer
Description of the IT research project: Crowdsourcing refers to the outsourcing of tasks to unknown workers via the Internet. In recent years, a large number of such crowdsourcing platforms have emerged, which offer a marketplace for the placement of tasks from employers to employees. They also provide additional mechanisms for handling employment relationships such as remuneration and control.
A special type of crowdsourcing platforms are micro-task markets such as Amazon Mechanical Turk. These micro-task markets are crowdsourcing platforms in which a large number of small tasks are performed by workers via the Internet.
An in-house used Micro-Task Market allows to face entrepreneurial challenges. Special skills of employees can be made available (e.g. between project teams or subsidiaries), resource bottlenecks can be balanced and available capacities can be made use of. This can be done effectively, for example, by taking into account skills, working hours, remuneration and motivation of employees in a recommendation system. The following application example shows how the approach of a company internal Micro-Task Market can be successful.
Let us assume that a department needs an Excel macro. But no employee in that department has the ability to create it. The task is therefore advertised company-wide via a platform. A recommendation system identifies an available and suitable employee from another department who will solve the task (for a certain remuneration or cost allocation). In doing so, a resource bottleneck was resolved in the first department, while available capacities from another department were used and the employee’s special skills were taken into account.
The aim of the project is to work out the special requirements for an internal crowdsourcing platform, to develop recommendation systems for a crowdsourcing platform and to use these to implement and test a prototype of such a platform.
Software Campus partners:TU Darmstadt, Holtzbrinck Publishing Group
Implementation period: 01.03.2016-31.12.2017
