QM for Crowd­sourcing

QM for crowdsourcing - establishment of a comprehensive quality management system for the use of crowd-based mechanisms by SMEs

 

Start­ing Situ­ation

Small and medium-sized enterprises (SMEs) are making increasing use of crowdsourcing. SMEs outsource certain tasks to a large number of potential Internet users (crowd) via open call via the Internet. The crowd takes on the processing of the tasks and submits contributions to solve them. When using crowd-based mechanisms, SMEs access the knowledge, creativity, manpower and resources of a large number of individuals in the production of operational services. For this reason, crowdsourcing is often understood as an innovative and digital form of work organization in companies. The spectrum of activities outsourced in crowdsourcing is broad: It includes both complex, creative and developmental activities (such as generating ideas / innovations, product and software development) and activities that are not or currently difficult to automate, such as the writing of product descriptions or translating and transcribing texts, up to rather simple activities such as marking pictures.

Despite the advantages presented above, this relatively new form of digital work organization also poses challenges for SMEs. These can be seen in particular in the quality of the work done by the crowd. Among other things, the question arises to what extent the quality of the crowd's contributions meets the expectations of the companies, i.e. whether they meet a certain quality standard. It is also questionable whether the crowd's contributions contain errors. The application practice of crowdsourcing reports, not only of unintentional errors, but even of deliberately placed errors in crowd posts. In addition, the question arises of how companies can encourage the crowd to deliver high-quality contributions whenever possible.

The question of the quality of the crowd contributions is highly relevant, as the crowd or crowd workers are largely unknown to the client company or even not known at all. Given this, it is difficult or even impossible for the company to assess the reliability, skills and loyalty of the crowd or crowdworkers. In addition, orders placed via the crowdsourcing mechanism tend to have little or no legal binding, as they usually cannot be secured by contracts or guarantees.

So far, however, there is a lack of concepts, instruments and practices for systematic requirements, error and incentive management, as well as knowledge of how such quality management can be integrated into existing processes and procedures in the company.

 

Aim of the pro­ject

The aim of the project is to establish a holistic quality management system, by means of which crowd-based work results can be used safely, efficiently and in a targeted manner in SMEs. This is done by adding, adapting and designing necessary interfaces (processes, methods and techniques) in the existing quality management as well as establishing and expanding it in the company departments involved (such as marketing, sales, development, but also IT) and the respective platforms or between departments.

For this purpose, a modular reference process model is being developed. The starting point is formed by already existing processes, methods and measures from the area of ​​quality management for the determination of requirements, for quality assurance and for error management, which are to be integrated, expanded and optimized as a component in an overall system.

Pro­ject Part­ners

  • Universität Kassel, Fachgebiet Qualitäts- und Prozessmanagement, Prof. Dr. Ing. Robert Refflinghaus
  • Universität Kassel, Fachgebiet Wirtschaftsinformatik, Prof. Dr. Jan Marco Leimeister

The IGF project 21758 N / 1 of the Forschungsvereinigung Forschungsgemeinschaft Qualität eV (FQS), August-Schanz-Straße 21A, 60433 Frankfurt am Main was funded by the Federal Ministry of Economics through the AiF as part of the program to promote industrial community research (IGF) and energy based on a resolution of the German Bundestag.

 

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