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26 NOVEMBER 2008

Knowledge Integration between Experts and Decision Makers

"Amidst the increasing complexity of markets, technologies, or consumer demands, ever more distributed expertise needs to be integrated for effective decision making. Consequently, the integration of knowledge becomes an important function for organizations [Grant 1996: 377]. Knowledge integration is the synthesis of individuals' specialized knowledge into situation–specific, systemic knowledge [Alavi and Tiwana 2002]. The aim of knowledge integration is not to minimize the knowledge gap between individuals, groups, or organizations, but to foster specialization while combining specialized knowledge in joint actions and decisions [Eisenhardt and Santos 2000]. Especially in complex, uncertain, and high–risk decision processes, managers need to draw on the specific knowledge of domain experts. Yet, the use of expertise is bound to cognitive, interactional, social, and political challenges that intervene in the decision making process [Eisenhardt and Zbaracki 1992]. In this paper, we focus on the interactional, i.e. communicative challenges of knowledge integration. By doing so, we aim to advance a communication perspective on knowledge management issues [see also: Mengis and Eppler 2005]. This perspective is based on the idea that we create, share, and integrate knowledge in social interactions [Nonaka and Takeuchi 1995] and that communication is therefore constitutive to knowledge processes. In the context of the expert–decision maker interaction, co–located conversations are the main communicative form through which knowledge is integrated. Conversations allow for high interactivity (participants can pose clarifying questions and ask for the larger context of a specific piece of information). The language and complexity of discourse can be finely aligned to the characteristics of the interlocutors [Krauss and Fussell 1991] and the para– and non–verbal cues facilitate the development of a common ground [Olson and Olson 2000], a prerequisite for mutual understanding.

On the other hand, conversations are ephemeral [Bregman and Haythornthwaite 2001] so that the major reasons and motivations behind the decisions taken are often poorly documented. They are bound to the linear flow of time, which limits comparisons of multiple variables and complex issues. Finally, conversations are often characterized by conversational patterns such as defensive arguing [Argyris 1996], unequal turn–talking [Ellinor and Gerard 1998], or dichotomous arguing [Tannen 1999].

In order to better utilize the potential of conversations for knowledge integration and to overcome the drawbacks and challenges that are bound to this communicational form, conversations can be supported by interactive visualization tools [Eppler 2005]. In this paper, we will hence discuss the role of collaborative visualization for knowledge integration by presenting an experimentally tested model."
(Jeanne Mengis & Martin J. Eppler, University of Lugano, Switzerland)


Simon Perkins

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