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I’ve begun writing a bit about netnography. For those of you who aren’t familiar with it, netnography is a set of guidelines for researching the communications, cultures, and communications that manifest through online or computer-mediated communications. I prepared a detailed wikipedia entry on it under virtual ethnography, but some smarmy undergrad ninny kept on editing out my entries. Isn’t that just a classic irony of online community?
Anyways, the method has been received enthusiastically and rapidly gained legitimacy as one of the premiere methods of investigating online communities and cultures within marketing. Netnographic guidelines have informed and spawned over a dozen thesis dissertations. There are currently over a dozen publications that list netnography as their primary methodology, and this number is growing quickly as many move through review. The method is featured in influential methodological volumes such as the Handbook of Qualitative Research Methods in Marketing and the Sage Dictionary of Social Research Methods and is inspiring a new generation of cultural researchers who almost automatically turn to the Internet as one of their field sites.
However, using the Internet as a field site created important challenges. The Internet is a dynamic forum of activity that is constantly shifting and changing. In the decade since I developed netnography, there have been many important changes to the research context that require investigation and further development of the method. I’m excited about the potential that these changes hold.
Netnography is faster, simpler, timelier, and much less expensive than traditional ethnography. Because its data is unelicited, it is more naturalistic and unobtrusive than focus groups, surveys, or interviews. However, my research and development have used Internet newsgroups—a forum whose use is declining—as the chief site of the method. Recently, I have been adapting the method to other online forums, such as Internet blogs (in the Belk Handbook chapter, above), but this development needs to continue and intensity. To remain relevant and useful, the method of netnography must continue to change and develop.
In addition, a range of for-profit companies have arisen that are using content analytic methods similar to and sometimes derived from netnography, including Accelovation, MotiveQuest, Cymfony, Umbria Communications, and Neilsen Buzzmetrics. These companies use software solutions to perform a type of data-mining operation on the qualitative information available on the Internet as a form of marketing research. Those methodologies are less selective and cultural than netnography, but offer greater representativeness and quantification of online data. We very much need to learn more about these companies and what they have to offer.
In past considerations of netnography, my research has considered that
How does the method of netnography account for the development of the Internet since the methods original development eleven years ago? How will it account for the blogosphere, virtual worlds, SNS, mobile, and software-driven content analytic research methods? How should netnography adapt to best serve contemporary researchers and practitioners?
Just as dozens of academics and a range of actual firms have used the original netnography method, so too will all of these complex forms and adaptations be needed and useful to a range of academics, such as those in marketing and consumer research, and to businesspeople, such as marketing researchers, consultants, marketing research firms, and companies with in-house marketing research, innovation, R&D, and other teams that benefit from having access to novel consumer insights.
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November 12, 2007 at 6:47 am
Robert,
we (me and some of my colleagues) have published two years ago a paper using data-mining in the orkut (www.orkut.com) virtual community. We have chosen 2 communities (”I love beer” with 52,000 members and “I hate beer” with 32,000 members) and made a probabilistic sample of them. We have “chosen” 400 users from each of these communities.
From the users, we have collected all the data that they show at their profile, as the number of communities that they participate, how many friends, how many scraps, testimonials, and other information that he or she can describe about himself or herself.
With these, we tryed to predict if the user participate in one community or in the other
We used two different types of data analysis: 1 - logistics regression; 2 - neural networks.
we got better results with the neural network, and some of the results from this analysis are these:
Training sample: 591
validation sample: 196
variables: 121
neurons: 124 with 3 occult layers
Rsquare: 0,7446
best index in training: 95% of the cases
best index in validation: 91% of the cases
it was a quite interesting paper, unfortunatly we just have it in portuguese.