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The IQ of the Crowd: Understanding and Improving Information Quality in Structured User-Generated Content

Author

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  • Roman Lukyanenko

    (College of Business, Florida International University, Miami, Florida 33199)

  • Jeffrey Parsons

    (Faculty of Business Administration, Memorial University of Newfoundland, St. John’s, Newfoundland A1B 3X5 Canada)

  • Yolanda F. Wiersma

    (Department of Biology, Memorial University of Newfoundland, St. John’s, Newfoundland A1B 3X5 Canada)

Abstract

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.

Suggested Citation

  • Roman Lukyanenko & Jeffrey Parsons & Yolanda F. Wiersma, 2014. "The IQ of the Crowd: Understanding and Improving Information Quality in Structured User-Generated Content," Information Systems Research, INFORMS, vol. 25(4), pages 669-689, December.
    • Handle: RePEc:inm:orisre:v:25:y:2014:i:4:p:669-689
    DOI: 10.1287/isre.2014.0537
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    References listed on IDEAS

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    1. Roman Lukyanenko & Andrea Wiggins & Holly K. Rosser, 0. "Citizen Science: An Information Quality Research Frontier," Information Systems Frontiers, Springer, vol. 0, pages 1-23.
    2. Aladwani, Adel M. & Dwivedi, Yogesh K., 2018. "Towards a theory of SocioCitizenry: Quality anticipation, trust configuration, and approved adaptation of governmental social media," International Journal of Information Management, Elsevier, vol. 43(C), pages 261-272.
    3. Lala Hajibayova, 2020. "(Un)theorizing citizen science: Investigation of theories applied to citizen science studies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(8), pages 916-926, August.
    4. Lala Hajibayova & L. P. Coladangelo & Heather A. Soyka, 2021. "Exploring the invisible college of citizen science: questions, methods and contributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6989-7003, August.
    5. Geng, Yuedan & Ye, Qiang & Jin, Yu & Shi, Wen, 2022. "Crowd wisdom and internet searches: What happens when investors search for stocks?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    6. Yang, Xi & Zhao, Quanwu & Sun, Heshan, 2022. "Seekers’ complaint behavior in crowdsourcing: An uncertainty perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    7. Yu-Kai Lin & Arun Rai & Yukun Yang, 2022. "Information Control for Creator Brand Management in Subscription-Based Crowdfunding," Information Systems Research, INFORMS, vol. 33(3), pages 846-866, September.
    8. Lorenz Graf-Vlachy & Tarun Goyal & Yannick Ouardi & Andreas König, 2021. "Reviews Left and Right: The Link Between Reviewers’ Political Ideology and Online Review Language," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 403-417, August.
    9. Minh-Tri Ha & Giang-Do Nguyen & Thi Huong-Thanh Nguyen & Bich-Duyen Nguyen, 2023. "The use of dietary supplements and vitamin consumption during and after the Covid pandemic in Vietnam: a perspective of user-generated content," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    10. Aladwani, Adel M., 2017. "Compatible quality of social media content: Conceptualization, measurement, and affordances," International Journal of Information Management, Elsevier, vol. 37(6), pages 576-582.
    11. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "The power of beauty: Be your ideal self in online reviews—an empirical study based on face detection," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    12. Yan Song & Xin Tian, 2020. "Managerial Responses and Customer Engagement in Crowdfunding," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    13. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    14. Roman Lukyanenko & Andrea Wiggins & Holly K. Rosser, 2020. "Citizen Science: An Information Quality Research Frontier," Information Systems Frontiers, Springer, vol. 22(4), pages 961-983, August.

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