KN 2008 : Knowledge Networks: Discovering network structure and patterns using Social Network Analysis
Call For Papers
During the last decade, knowledge has become a key consideration in our economies and it is heavily associated with innovation. Alongside this, so-called knowledge networks have arguably come to play a central role in organizations. These networks, which are built on social relations between employees, might serve various purposes such as collaborative problem solving, seeking advice, or developing competences by learning from peers. Recently, the network perspective has gained interest in the domain of knowledge networks and involves the study of the structure and patterns of knowledge networks. These studies rely heavily on theory and tools from social network analysis that has already a longstanding tradition in the sociology domain. The tools and techniques developed in this domain can be fruitfully applied in the field of knowledge networks. These techniques and tools can be used as (1) assessment tools and (2) research instruments.
As an assessment tool, social network analysis can be used to visualize and analyze for instance the advice seeking relations in an organization. Through visual inspection of the sociogram and by calculating network metrics such as average shortest path, connectedness or centralization, it is possible to detect potential bottlenecks in the advice seeking network of the organization. However, there is not much consensus yet about the network metrics that should be used. Furthermore, there is hardly any benchmark data available to determine e.g. if a certain value for the average shortest path is too high or not. Finally, in many cases it is not known what the structure of an ideal knowledge network looks like and how a particular instance of a knowledge network is deviating from that ideal state.
Secondly, social network analysis can also be used as a research instrument to study how the structure and patterns of knowledge networks are related to other variables such as task, group and organizational performance or to demographic data of people in the network. Much of the research so far has focused on a limited number of network metrics that are typically studied individually: interaction effects are neglected. Furthermore, group level performance also did not receive much attention yet.
Regardless of how social network analysis is used, in both cases it is necessary to collect social network data. This is typically done using interviews and/or surveys, which is a labor intensive task. Recently, also other ways of collecting network data have been explored such as mining e-mail traffic (only header or also content information) or the archives of online community forums. The advantage of this way of data collection is that it is less labour intensive and at the same time offers to do longitudinal analysis for studying the dynamics of knowledge networks. However, there has not been much research that studies if communication networks, i.e. e-mail traffic, actually resemble knowledge networks.
The workshop aims to bring together researchers from different disciplines that are interested in the application of social network analysis tools and techniques in knowledge network research. As such, the workshop provides a platform: 1) to elaborate on frequently used and emerging research questions and 2) to evaluate methodology used and, 3) to compare results of different research settings. We welcome both theoretical and empirical papers that employ diverse methodologies and philosophical perspectives.
Suggested topics (but not limited to this list)
* Influence of network position/network pattern on individual/ group/organizational performance
* Tools for harvesting mail messages and online communities for knowledge network data
* Case studies concerning the application of SNA to study knowledge networks in organizations
* Research that studies the factors that influence the formation of knowledge networks
* Case studies concerning the analysis of knowledge networks based on e-mail data
* Relation of knowledge networks to other networks such as friendship or communication networks
* Tools that track, map, and visualize knowledge networks
* Dynamics of networks, i.e. evolution of networks over time
* Application of cluster algorithms to detect knowledge networks/communities of practice in social network data
* Effects of social network technologies on the structure of knowledge networks
* Remko Helms, Faculty of Science, Department of Information and Computing Sciences, Utrecht University, Netherlands (firstname.lastname@example.org)
* Miha Škerlavaj, Faculty of Economics, Department for Management and Organization, University of Ljubljana, Slovenia (email@example.com)
* Allatta Joan, Purdue University, United States
* Andrea Back, University of St Gallen, Switzerland
* Elaine Ferneley, University of Salford, United Kingdom
* Emmanuel Lazega, University of Lille I, France
* Claudia Müller, University of Stuttgart, Germany
* Tobias Müller-Prothman, Pumacy Technologies AG, Germany
* Jurriaan van Reijsen, Utrecht University, Netherlands
* Chunke Su,University of Texas at Arlington, United States
* Robin Teigland, Stockholm School of Economics, Sweden
* Remko Helms: firstname.lastname@example.org
* Miha Škerlavaj: email@example.com
Currently we are investigating if there is a possibility to publish the best papers in a special issue of a journal, e.g. Journal of Knowledge Management or Knowledge Management Research and Practice.