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Easier Crowdsourcing Is Better: Designing Crowdsourcing Systems to Increase Information Quality and User Participation

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Design Science Research. Cases

Part of the book series: Progress in IS ((PROIS))

Abstract

Crowdsourcing promises to expand organizational knowledge and “sensor” networks dramatically, making it possible to engage ordinary people in large-scale data collection, often at much lower cost than that of traditional approaches to gathering data. A major challenge in crowdsourcing is ensuring that the data that crowds provide is of sufficient quality to be usable in organizational decision-making and analysis. We refer to this challenge as the Problem of Crowd Information Quality (Crowd IQ). We need to increase quality while giving contributors the flexibility to contribute data based on their individual perceptions. The design science research project produced several artifacts, including a citizen science information system (NLNature), design principles (guidelines) for the development of crowdsourcing projects, and an instance-based crowdsourcing design theory. We also made several methodological contributions related to the process of design science research and behavioral research in information systems. Over the course of the project, we addressed several challenges in designing crowdsourcing systems, formulating design principles, and conducting rigorous design science research. Specifically, we showed that: design choices can have a sizable impact in the real world; it can be unclear how to implement design principles; and design features that are unrelated to design principles can confound efforts to evaluate artifacts. During the project, we also experienced challenges for which no adequate solution was found, reaffirming that design is an iterative process.

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Notes

  1. 1.

    https://ebird.org/region/world.

  2. 2.

    While basic-level categories are among the most researched notions in cognitive psychology (Lassaline, Wisniewski, & Medin, 1992), our realization that this notion lacks operational precision precipitated development of a separate DSR project to formalize the notion of basic-level categories, provide clear and unambiguous principles for discovering and identifying these classes in a domain, and provide practitioners with principles for using these classes in the design, development and use of information systems (Castellanos, Castillo, Lukyanenko, & Tremblay, 2017; Castellanos, Lukyanenko, Samuel, & Tremblay, 2016; Lukyanenko & Samuel, 2017).

  3. 3.

    This is a simplified implementation. In the real schema, additional attributes were included in each table, including additional semantic attributes and a variety of system attributes like time stamp, IP address, system properties of the record creator, security, validation, and monitoring keys.

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Authors and Affiliations

  1. HEC Montreal, Montreal, Canada

    Roman Lukyanenko

  2. Memorial University of Newfoundland, St. John’s, Canada

    Jeffrey Parsons

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  1. Department of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

  2. Muma College of Business, University of South Florida, Tampa, FL, USA

    Alan Hevner

  3. Institute of Information Systems and Marketing (IISM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Alexander Maedche

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Lukyanenko, R., Parsons, J. (2020). Easier Crowdsourcing Is Better: Designing Crowdsourcing Systems to Increase Information Quality and User Participation. In: vom Brocke, J., Hevner, A., Maedche, A. (eds) Design Science Research. Cases. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-030-46781-4_3

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