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GOR 2001 - content This is the http://kiwi.uni-psych.gwdg.de/congress/gor-2001/contrib/diemers-daniel/diemers-daniel Document. Main Author: Diemers, Daniel Co-Authors: ; Institution: Institute for Sociology, University of St. Gallen Contribution Title: VISAM. A Methodology for an internet-based early warning system. Authors Email: daniel.diemers@unisg.ch URLs: http://www.sfs.unisg.ch Abstract German (version: 25/06/2002 - 07:47, size: 0) English: The business environment of the early 21st century is marked by high degrees of uncertainty, market volatility, and information overload. The process of virtualization creates increasing social, economic, and political dynamics in virtual spaces, which are becoming parallel interaction spaces as an socially embedded alternative to our physical, daily life-world. For large global companies it is existentially important that these developments are identified, discussed and responded with appropriate organizational measures in a short period of time. Recent examples of companies that were caught unexpectedly by these developments have shown that pressure originating in virtual spaces and virtual communities may eventually threaten even large companies. This contribution discusses a practical conceptualization of VISAM, an internet-based early-warning system developed on the theoretical foundations of the newer sociology of knowledge. The main goal of VISAM is to extend a company's perception of its business environment into virtual spaces. It that sense, VISAM is an intra-organizational early warning system directed towards processes in virtual spaces. VISAM combines an IT-based infrastructural set of measures with an organizational, soft-skill methodology. In this contribution the author will give a general overview over the theoretical conceptualization of VISAM, and elaborate on the first practical experiences of the implemented version of a VISAM-pilot. Article preliminary article (version: 25/06/2002 - 07:47, size: 512) view preliminary article - edit preliminary article - upload an image(for authors only)