As promised, here's my analysis of Michael Habib's Master's thesis "Toward Academic Library 2.0: Development and Application of a Library 2.0 Methodology" paper after a more thoroughly careful reading a second time. Habib's thesis astutely asserts that Web 2.0 has seven main concepts. Here they are:
(1) The Read/Write Web - A term given to describe the main differences between Old Media (newspaper, radio, and TV) and New Media (e.g. blogs, wikis, RSS feeds), the new Web is dynamic in that it allows consumers of the web to alter and add to the pages they visit - information flows in all directions.
(2) The Web as a Platform - Better known as "perpetual beta," the idea behind Web 2.0 services is that they need to be constantly updated. Thus, this includes experimenting with new features in a live environment to see how customers react.
(3) The Long Tail - The new Web lowers the barriers for publishing anything (including media) related to a specific interest because it empowers writers to connect directly with international audiences interested in extremely narrow topics, whereas originally it was difficult to publish a book related to a very specific interest because its audience would be too limited to justify the publisher's investment.
(4) Harnessing Collective Intelligence - Google, Amazon, and Wikipedia are good examples of how successful Web 2.0-centric companies use the collective intelligence of users in order to continually improve services based on user contributions. Google's PageRank examines how many links points to a page, and from what sites those links come in order to determine its relevancy instead of the evaluating the relevance of websites based solely on their content.
(5) Network Effects - It is a concept which explains why social technologies benefit from an economy that awards value to the service as more people join the service. eBay is one example of how the application of this concept works so successfully.
(6) Core Datasets from User Contributions - Web 2.0 companies use to collect unique datasets is through user contributions. However, collecting is only half the picture; using the datasets is the key. These contributions are then organized into databases and analyzed to extract the collective intelligence hidden in the data. This extracted information is then used to extract collective knowledge that can be applied to the direct improvement of the website or web service.
(7) Lightweight Programming Models - The move toward database driven web services has been accompanied by new software development models that often lead to greater flexibility. In sharing and processing datasets between partners, this enables mashups and remixes of data. Google Maps is a common example as it allows people to combine its data and application with other geographic datasets and applications.
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