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Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration

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Listed:
  • Quentin Jones

    () (Department of Information Systems, College of Computing Sciences, New Jersey Institute of Technology, University Heights, Newark, New Jersey 07102)

  • Gilad Ravid

    () (Center for the Study of the Information Society, Graduate School of Business Administration, University of Haifa, Mt. Carmel, Haifa 31905, Israel)

  • Sheizaf Rafaeli

    () (Center for the Study of the Information Society, Graduate School of Business Administration, University of Haifa, Mt. Carmel, Haifa 31905, Israel)

Abstract

Online spaces that enable shared public interpersonal communications are of significant social, organizational, and economic importance. In this paper, a theoretical model and associated unobtrusive method are proposed for researching the relationship between online spaces and the behavior they host. The model focuses on the collective impact that individual information-overload coping strategies have on the dynamics of open, interactive public online group discourse. Empirical research was undertaken to assess the validity of both the method and the model, based on the analysis of over 2.65 million postings to 600 Usenet newsgroups over a 6-month period. Our findings support the assertion that individual strategies for coping with “information overload” have an observable impact on large-scale online group discourse. Evidence was found for the hypotheses that: (1) users are more likely to respond to simpler messages in overloaded mass interaction; (2) users are more likely to end active participation as the overloading of mass interaction increases; and (3) users are more likely to generate simpler responses as the overloading of mass interaction grows.The theoretical model outlined offers insight into aspects of computer-mediated communication tool usability, technology design, and provides a road map for future empirical research.

Suggested Citation

  • Quentin Jones & Gilad Ravid & Sheizaf Rafaeli, 2004. "Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration," Information Systems Research, INFORMS, vol. 15(2), pages 194-210, June.
  • Handle: RePEc:inm:orisre:v:15:y:2004:i:2:p:194-210
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    File URL: http://dx.doi.org/10.1287/isre.1040.0023
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    References listed on IDEAS

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    1. Lee Sproull & Sara Kiesler, 1986. "Reducing Social Context Cues: Electronic Mail in Organizational Communication," Management Science, INFORMS, vol. 32(11), pages 1492-1512, November.
    2. Brian S. Butler, 2001. "Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures," Information Systems Research, INFORMS, vol. 12(4), pages 346-362, December.
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    Cited by:

    1. Jae Yun Moon & Lee S. Sproull, 2008. "The Role of Feedback in Managing the Internet-Based Volunteer Work Force," Information Systems Research, INFORMS, vol. 19(4), pages 494-515, December.
    2. Mehmet N. Aydin & N. Ziya Perdahci, 0. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    3. Richard B. Freeman & M. Marit Rehavi, 2009. "Helping Workers Online and Offline: Innovations in Union and Worker Organization Using the Internet," NBER Chapters,in: Studies of Labor Market Intermediation, pages 273-306 National Bureau of Economic Research, Inc.
    4. Xiaoqing Wang & Brian S. Butler & Yuqing Ren, 2013. "The Impact of Membership Overlap on Growth: An Ecological Competition View of Online Groups," Organization Science, INFORMS, vol. 24(2), pages 414-431, April.
    5. Lu (Lucy) Yan & Jianping Peng & Yong Tan, 2015. "Network Dynamics: How Can We Find Patients Like Us?," Information Systems Research, INFORMS, vol. 26(3), pages 496-512, September.
    6. Bin Gu & Prabhudev Konana & Rajagopal Raghunathan & Hsuanwei Michelle Chen, 2014. "Research Note —The Allure of Homophily in Social Media: Evidence from Investor Responses on Virtual Communities," Information Systems Research, INFORMS, vol. 25(3), pages 604-617, September.
    7. repec:gam:jsusta:v:10:y:2018:i:9:p:3185-:d:168081 is not listed on IDEAS
    8. Ivo Blohm & Christoph Riedl & Johann Füller & Jan Marco Leimeister, 2016. "Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing," Information Systems Research, INFORMS, vol. 27(1), pages 27-48, March.
    9. Cumiks Aleksejs & Romanovs Andrejs, 2012. "The Analysis and Modelling of Social Networks: Leader Identification and Information Dissemination," Information Technology and Management Science, Sciendo, vol. 15(1), pages 127-133, December.
    10. Bin Gu & Prabhudev Konana & Balaji Rajagopalan & Hsuan-Wei Michelle Chen, 2007. "Competition Among Virtual Communities and User Valuation: The Case of Investing-Related Communities," Information Systems Research, INFORMS, vol. 18(1), pages 68-85, March.
    11. Brian S. Butler & Xiaoqing Wang, 2012. "The Cross-Purposes of Cross-Posting: Boundary Reshaping Behavior in Online Discussion Communities," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 993-1010, September.
    12. Samer Faraj & Steven L. Johnson, 2011. "Network Exchange Patterns in Online Communities," Organization Science, INFORMS, vol. 22(6), pages 1464-1480, December.
    13. Patrick J. Bateman & Peter H. Gray & Brian S. Butler, 2011. "Research Note ---The Impact of Community Commitment on Participation in Online Communities," Information Systems Research, INFORMS, vol. 22(4), pages 841-854, December.
    14. Tetyana Kuvita & Miroslav Karlíček, 2014. "The Risk of Vampire Effect in Advertisements Using Celebrity Endorsement," Central European Business Review, University of Economics, Prague, vol. 2014(3), pages 16-22.
    15. Oliver Hinz & Martin Spann, 2008. "The Impact of Information Diffusion on Bidding Behavior in Secret Reserve Price Auctions," Information Systems Research, INFORMS, vol. 19(3), pages 351-368, September.
    16. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.

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