CorpWiki: A self-regulating wiki to promote corporate collective intelligence through expert peer matching

  • Authors:

  • Ioanna Lykourentzou;Katerina Papadaki;Dimitrios J. Vergados;Despina Polemi;Vassili Loumos

  • Affiliations:

  • School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Informatics, University of Pireaus, Karaoli and Dimitriou 80, 18532 Pireaus, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

One of the main challenges that organizations face nowadays, is the efficient use of individual employee intelligence, through machine-facilitated understanding of the collected corporate knowledge, to develop their collective intelligence. Web 2.0 technologies, like wikis, can be used to address the above issue. Nevertheless, their application in corporate environments is limited, mainly due to their inability to ensure knowledge creation and assessment in a timely and reliable manner. In this study we propose CorpWiki, a self-regulating wiki system for effective acquisition of high-quality knowledge content. Inserted articles undergo a quality assessment control by a large number of corporate peer employees. In case the quality is inadequate, CorpWiki uses a novel expert peer matching algorithm (EPM), based on feed-forward neural networks, that searches the human network of the organization to select the most appropriate peer employee who will improve the quality of the article. Performance evaluation results, obtained through simulation modeling, indicate that CorpWiki improves the final quality levels of the inserted articles as well as the time and effort required to reach them. The proposed system, combining machine-learning intelligence with the individual intelligence of peer employees, aims to create new inferences regarding corporate issues, thus promoting the collective organizational intelligence.