Hans Thies

Statistical Quality Control for Human-Based Electronic Services

Crowdsourcing in form of human-based electronic services (people services) provides a powerful way of outsourcing tasks to a large crowd of remote workers over the Internet. Research has shown that multiple redundant results delivered by different workers can be aggregated in order to achieve a reliable result. However, existing implementations of this approach are rather inefficient as they multiply the effort for task execution and are not able to guarantee a certain quality level. As a starting point towards an integrated approach for quality management of people services we have developed a quality management model that combines elements of statistical quality control (SQC) with group decision theory. The contributions of the workers are tracked and weighted individually in order to minimize the quality management effort while guaranteing a well-defined level of overall result quality. A quantitative analysis of the approach based on an optical character recognition (OCR) scenario confirms the efficiency and reach of the approach.

Quality assurance for human-based electronic services: a decision matrix for choosing the right approach

Crowdsourcing in the form of human-based electronic services provides a powerful way of outsourcing so called human intelligence tasks (HITs) to a large workforce of people over the Internet. Because of the limited control over that workforce, it is challenging to ensure the quality of the work results. Several approaches have been proposed that can be applied to specific types of HITs. However, it is difficult to identify a suitable quality management approach for any given type of HIT. This paper aims to provide a first sketch of a decision matrix.

Dynamic and goal-based quality management for human-based electronic services

Crowdsourcing in the form of human-based electronic services (people services) provides a powerful way of outsourcing tasks to a large crowd of remote workers over the Internet. Research has shown that multiple redundant results delivered by different workers can be aggregated in order to achieve a reliable result. However, basic implementations of this approach are rather inefficient as they multiply the effort for task execution and are not able to guarantee a certain quality level. In this paper, we are addressing these challenges by elaborating on a statistical approach for quality management of people services which we had previously proposed. The approach combines elements of statistical quality management with dynamic group decisions. We present a comprehensive statistical model that enhances our original work and makes it more transparent. We also provide an extendible toolkit that implements our model and facilitates its application to real-time experiments as well as to simulations. A quantitative analysis based on an optical character recognition (OCR) scenario confirms the efficiency and reach of our model.

Enhancing the Quality of Information in Inter-organizational Environmental Reporting Information Systems

Environmental reporting has become standard within big and medium-sized international companies. However, organizations face many knowledge management challenges in environmental reporting, such as data availability, comparability, flexibility, and high costs. Some of these challenges can be mitigated by inter-organizational environmental reporting information systems (IO-ERIS). Nevertheless, the information quality remains a challenge. This research aims at identifying the state of the art in environmental reporting, extracting the requirements for information quality in environmental reporting, and proposes an approach of how the quality of environmental information in IO-ERIS can be enhanced following the design science research cycle.

The Potential of a Network-Centric Solution for Sustainability in Business Processes

Due to an increasing pressure from international regulation, customers and other stakeholders, companies are increasingly experiencing the need to incorporate sustainability considerations in their core business processes and daily operations. For this purpose they require software solutions that simplify the collection, analysis, and incorporation of sustainability indicators at the right processes across their operations. However, prevailing systems are enterprise-centric in the sense that they are owned and used by one focal company collecting the data from different sources and using it for its internal decision making. This paper will describe three example use cases in which sustainability plays a key role and will provide an overview of major problems with the current state of the art. In the second part of the paper, a new approach for sharing sustainability indicators is introduced that enables many providers and consumers of environmental data to connect to and leverage a common platform. Finally, the paper analyzes the potential risks and benefits of introducing such a network platform, using the three business use cases to illustrate the opportunities resulting from it.

Incorporating Supplier Data

Network participation is a crucial factor in order to reap the benefits of a system like OEPI which is based on user-generated content. Therefore, motives for organizations to join the network and contribute content have to be identified in order to develop incentives that can make the network viral. This chapter identifies the organizational and individual motives, develops incentives based on these, and finally presents the basic concept of how network participation can be stimulated.