E-Memory Choice Architecture: Modeling the Use Diffusion of Twitter Archiving System

E-Memory Choice Architecture: Modeling the Use Diffusion of Twitter Archiving System

Hsia-Ching Chang (University of North Texas, Denton, USA) and Chen-Ya Wang (Department of Management & Information, National Open University, New Taipei City, Taiwan)
Copyright: © 2019 |Pages: 14
DOI: 10.4018/IJOM.2019010102


Twitter archiving systems have been developed to preserve users' tweets. The available methods of organizing tweets for curation include the hashtag, user ID, and keywords. These can be viewed as memory encoding symbols supporting future retrieval of users' social media memories. As Twitter has become a global social media platform, online Twitter archiving systems have transformed from an open platform for archiving tweets to an integrated service managing multiple accounts across platforms. With the changing business models of Twitter archiving systems, usage data has become unavailable publicly. This study collected historical usage data from the API of an online Twitter archiving system, TwapperKeeper, before its acquisition by Hootsuite in September 2011. The valuable system usage data allowed this study to examine the tweet archiving preferences of early Twitter adopters. By mapping adoption-diffusion and use-diffusion models into the web information architecture of the online archiving system, this study analyzed user choice architecture through the system function use.
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Literature Review

Measurement of Web Information Architecture

As an architect, Wurman (1996) infused architecture with new meaning in the information environment, and coined the term information architecture (IA), defining it as “organizing the patterns in data.” A panel held at the ACM Conference on Human Factors in Computing Systems in 2001 discussed measurement issues in information architecture; researchers and practitioners hold different views on two semi-independent dimensions for which information architecture can be measured: theoretical or practical directions and quantifiable or non-quantifiable elements. One of the panelists, Marchionini, took the central position and suggested that three criteria, namely granularity, tasks and people, determine the approach to measuring IA. Hearst’s position focused on matching the quantifiable IA elements with applicable theoretical framework and analysis methods. This study adopted Hearst’s perspective to conduct the research design.

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