also sprach Gabriella Coleman <biella@gmail.com> [2007.09.25.1415 +0100]: > intelligence to the wider community. It is chapter four of my > dissertation, which I will pass along to you if you would like. I have your entire dissertation; thanks for the pointer! > Also there is a long literature on hacking which notes the > semi-obessive traits of hackers, which I would say also pertains > to DD's. Turkle and Levy are two folks that come to mind. Yeah, I am aware of those. What I am trying to do is actually argue my way out of having to analyse other groups as well, so therefore I am trying to narrow down the subject group a bit more than "Hackers" :) I guess the same could be said about Benkler: I am not talking about motivation, not about why a DD does what he does, but rather why s/he would do change the way s/he is doing something. Thus, this is innovation research, the question about diffusions and adoptions. There are plenty of factors which play a role in this, and I have started to collect these *from the point of view of a Debian developer*: http://phd.martin-krafft.net/wiki/factors/ . I intend to also announce this list soon and let others comment, then possibly also running a Delphi method on it. If I don't limit myself to Debian developers, then I could spend another 5 years collecting factors. I already feel like after several weeks on this, I am far from the goal... While I'm at it, here's my current plan, just in case anyone's interested and/or wants to comment: A framework to assess diffusions can be used to "compute" (qualitatively) the likelihood of a speedy adoption of a tool in a subject community, given attributes of the tool, the community, the way it was spread, etc. Given such a framework (to be selected, e.g. Rogers), which I use as a starting point, and a diffusion of a tool in Debian (e.g. debhelper), I want to modify the framework in such a way as to minimise the discrepancy between projected adoption rate and actual adoption rate (which closely relates to success of the tool, I guess), using developer traits to steer and back up the modifications. After several iterations and case studies, I hope to be able to have condensed a framework which can be used to predict (and thus engineer) the adoption rate of a tool in the Debian developer community. I could go and look at other communities (e.g. Apache, kernel, Plone, FreeBSD, etc.), but it'd be hard to deal with the bias, given how immersed I am in the Debian community and how little I know the others. I'd prefer to leave application of my framework to other projects up for future research. Comments welcome, -- Martin F. Krafft <mailto:martin.krafft@ul.ie> Ph.D. student http://phd.martin-krafft.net Lero - the Irish Software Engineering Research Centre University of Limerick, Ireland
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