Ulrike, The topic you are interested in would be very suited to a Communities of Practice approach. This definition is from the website of Etienne Wenger (http://www.ewenger.com/theory/index.htm). He and Jean Lave came up with the term. As you can see from the definition, CoP is about how groups form and develop, which seems to be what you are interested in. As for specific research methodologies, I'd also recommend looking at sources like Murielle Saville-Troike's "Ethnography of Communication." While this does not specifically deal with on-line activity, the methodologies are still quite relevant/adaptable. Three characteristics are crucial: 1. */ The domain: /* A community of practice is not merely a club of friends or a network of connections between people. It has an identity defined by a shared domain of interest. Membership therefore implies a commitment to the domain, and therefore a shared competence that distinguishes members from other people. (You could belong to the same network as someone and never know it.) The domain is not necessarily something recognized as "expertise" outside the community. A youth gang may have developed all sorts of ways of dealing with their domain: surviving on the street and maintaining some kind of identity they can live with. They value their collective competence and learn from each other, even though few people outside the group may value or even recognize their expertise. 2. */ The community: /* In pursuing their interest in their domain, members engage in joint activities and discussions, help each other, and share information. They build relationships that enable them to learn from each other. A website in itself is not a community of practice. Having the same job or the same title does not make for a community of practice unless members interact and learn together. The claims processors in a large insurance company or students in American high schools may have much in common, yet unless they interact and learn together, they do not form a community of practice. But members of a community of practice do not necessarily work together on a daily basis. The Impressionists, for instance, used to meet in cafes and studios to discuss the style of painting they were inventing together. These interactions were essential to making them a community of practice even though they often painted alone. 3. */ The practice: /* A community of practice is not merely a community of interest--people who like certain kinds of movies, for instance. Members of a community of practice are practitioners. They develop a shared repertoire of resources: experiences, stories, tools, ways of addressing recurring problems—in short a shared practice. This takes time and sustained interaction. A good conversation with a stranger on an airplane may give you all sorts of interesting insights, but it does not in itself make for a community of practice. The development of a shared practice may be more or less self-conscious. The "windshield wipers" engineers at an auto manufacturer make a concerted effort to collect and document the tricks and lessons they have learned into a knowledge base. By contrast, nurses who meet regularly for lunch in a hospital cafeteria may not realize that their lunch discussions are one of their main sources of knowledge about how to care for patients. Still, in the course of all these conversations, they have developed a set of stories and cases that have become a shared repertoire for their practice. Randall Sadler Ulrike Pfeil wrote:

Hello everybody, I am interested in doing longitudinal qualitative analysis of asynchronous online communities in order to analyse the development of the online community over time (e.g. looking at how people develop a sense of community, how communication and interaction patterns change over time, how people take on or abandon certain roles within the community etc.).

However, I am having a hard time finding methodological guidance on how to go about it (the longitudinal part). Does anybody have experience with longitudinal qualitative analysis of CMC or can recommend literature that might help me? Any help would be greatly appreciated!

Kind regards,

Uli

-- Randall Sadler Assistant Professor University of Illinois at Urbana-Champaign Division of English as an International Language 3080 Foreign Languages Building, MC-172 707 S. Mathews Urbana, IL 61801 USA

Hi, Our research group has been studying dynamics of free and open source software communities, which stand somewhere between work teams and online communities (or perhaps they are both, not sure of your definition :). Our approach is outlined in this NSF grant: <http://floss.syr.edu/proposals/2005hsd.pdf> Methodologically, we've used content analysis of online archives, using hybrid developed schemas (ie theory + induction) which are then applied in a fairly positivist way. We've also used social network analysis based on these archives. We're building up to Computational Natural Language Processing approaches for large scale studies. We're still working up our longitudinal approaches, so far we've primarily worked through periodization, identifying relevant periods (such as beginning, v1.0 and recent, or end for failing teams). You can see that approach in our study of decision making: Heckman, R., Crowston, K., Li, Q., Allen, E., Eseryel, U. Y., Howison, J., and Wei, K. (2006). Emergent decision-making practices in technology-supported self-organizing distributed teams. In International Conference on Information Systems (ICIS 2006). <http://floss.syr.edu/publications/Heckman2006Emergent_Decision-making_Practi...

More recently we've begun to look at small periods, such as months, and creating time-series for the prevalence of particular behaviors (such as group maintenance behaviors), then comparing those time- series to time-series of effectiveness measures. One big issue is whether to think in terms of calendar time or event time. Our teams are 'part-time' for participants, so one can't assume constant effort; this means one has to normalize observations of behavior in some way, so event time really starts to make sense. We're exploring inter- release periods as the 'natural periodization' for our teams, but other communities may have different 'zeitgebers'. You can see a monthly approach in our SNA analysis work (other papers cited from this one): Wiggins, Andrea and James Howison and Kevin Crowston (2008) Social dynamics of FLOSS team communication across channels, Submitted to 'Fourth International Conference on Open Source Software (IFIP 2.13)' <http://floss.syr.edu/StudyP/DSNAWigginsIFIP.pdf> Another big challenge, for those seeking to quantify change over time in these communities, is how to deal with the auto-correlation in time- series, through techniques such as ARIMA regression models, which is pretty important if you intend to use regression with the time-series as variables. For my dissertation I am examining organizational change by looking at the genre of documents produced by free and open source teams over time, and comparing time-series of relative genre use to effectiveness measures over time. Finally I'm writing narratives of how those genres (and genre-systems) emerged and why they, and their relative use, change over time. <http://james.howison.name/pubs/howison_proposal_abstract.pdf> There's also this excellent paper which examines the emergence of participation in a FLOSS project: von Krogh, G., Spaeth, S. Lakhani, K. R. 2003, ‘Community, joining, and specialization in open source software innovation: a case study’, Research Policy 32(7), 1217–1241. For a larger-scale, non content analysis approach, you might find this study interesting: Christley, S. Madey, G. 2007, Global and temporal analysis of social positions at sourceforge.net, in ‘The Third International Conference on Open Source Systems (OSS 2007), IFIP WG 2.13’, Limerick, Ireland. <http://www.nd.edu/~oss/Papers/oss2007_temporal.pdf> Finally, Hala Annabi, now at Ohio University, also studying group learning over time in the Apache project. --J On Jan 31, 2008, at 8:38 AM, Ulrike Pfeil wrote:

Hello everybody, I am interested in doing longitudinal qualitative analysis of asynchronous online communities in order to analyse the development of the online community over time (e.g. looking at how people develop a sense of community, how communication and interaction patterns change over time, how people take on or abandon certain roles within the community etc.).

However, I am having a hard time finding methodological guidance on how to go about it (the longitudinal part). Does anybody have experience with longitudinal qualitative analysis of CMC or can recommend literature that might help me? Any help would be greatly appreciated!

Kind regards,

Uli -- Psssst! Schon vom neuen GMX MultiMessenger gehört? Der kann`s mit allen: http://www.gmx.net/de/go/multimessenger _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org

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