Exploring the dynamic contribution behavior of editors in wikis based on time series analysis. Issue 1 (1st February 2016)

• Record Type: Journal Article
• Authors: Huang, Linghe; Zhu, Qinghua; Du, Jia Tina; Lee, Baozhen
• Is Part Of: Program. Volume 50:Issue 1(2016)
• Page Start: 41
• Page End: 57
• Publication Date: 2016-02-01
• Subjects: Time series -- Contribution behavior -- Dynamic characteristics -- Editor types -- Wiki editors; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-06-2013-0034
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, "free rider problem" has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach: – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings: – There are four categories of editors: "testers, " "dropouts, " "delayers" and "stickers." Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors' contributions. Originality/value: – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.