No abstract

The rapid proliferation of online content producing and sharing technologies resulted in an explosion of user-generated content (UGC), which now extends to scientific data. Citizen science, in which ordinary people contribute information for scientific research, epitomizes UGC. Citizen science projects are typically open to everyone, engage diverse audiences, and challenge ordinary people to produce data of highest quality to be usable in science. This also makes citizen science a very exciting area to study both traditional and innovative approaches to information quality management. With this paper we position citizen science as a leading information quality research frontier. We also show how citizen science opens a unique opportunity for the information systems community to contribute to a broad range of disciplines in natural and social sciences and humanities.

User-generated content (UGC) is becoming a valuable organizational resource, as it is seen in many cases as a way to make more information available for analysis. To make effective use of UGC, it is necessary to understand information quality (IQ) in this setting. Traditional IQ research focuses on corporate data and views users as data consumers. However, as users with varying levels of expertise contribute information in an open setting, current conceptualizations of IQ break down. In particular, the practice of modeling information requirements in terms of fixed classes, such as an Entity-Relationship diagram or relational database tables, unnecessarily restricts the IQ of user-generated data sets. This paper defines crowd information quality (crowd IQ), empirically examines implications of class-based modeling approaches for crowd IQ, and offers a path for improving crowd IQ using instance-and-attribute based modeling. To evaluate the impact of modeling decisions on IQ, we conducted three experiments. Results demonstrate that information accuracy depends on the classes used to model domains, with participants providing more accurate information when classifying phenomena at a more general level. In addition, we found greater overall accuracy when participants could provide free-form data compared to a condition in which they selected from constrained choices. We further demonstrate that, relative to attribute-based data collection, information loss occurs when class-based models are used. Our findings have significant implications for information quality, information modeling, and UGC research and practice.

No abstract

This appendix describes our applicability check in more detail. The purpose of the applicability check (Rosemann and Vessey 2008) was to determine whether attribute data could be transformed to a form (in this case, species level classification) useful to data consumers (in this case, biologists). We also used the applicability check to explore perceptions that biologists in a university setting held about the potential uses and usefulness of data collected using an instance-based approach (versus a class-based approach). The applicability check is discussed briefly in the main manuscript; here, we provide details about the method we used to collect data, and the feedback we received from participants. Method We tested the applicability of an attribute-based data collection approach to users of UGC via an interactive seminar presentation. We made the presentation as part of a seminar series in the Department of Geography at Memorial University of Newfoundland, as geography is a field in which there is considerable interest in crowdsourced UGC (referred to by geographers as volunteered geographic information) and none of the authors are affiliated with the department. We developed a questionnaire, which was distributed in paper form, on the tables where audience members sat. The questionnaire asked six open-ended questions about the perceived benefits and limitations of both instance-based versus class-based approaches, as well as about potential applications of the instance-based approach to the respondent's own research. In addition, there were two questions asking respondents to rank (on a seven-point Likert scale) their agreement with two statements, one about the relevance and applicability of the instance-based data collection approach and the other about the relevance and applicability of the experimental findings we presented. The questionnaire also included some biographical questions (gender, position, research field, highest degree obtained).

No abstract

No abstract

Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual steps of the literature review process. Considering that the use of AI in this context is in an early stage of development, we propose a comprehensive research agenda for AI-based literature reviews (AILRs) in our field. With this agenda, we would like to encourage design science research and a broader constructive discourse on shaping the future of AILRs in research.