Use Case Description – What happens?
Activity – Making discovery more convenient by bringing everything together in one place (a ‘one stop shop’) may be a Utopian dream. However, as illustrated by the Amazon service, there may be value in developing a ‘first stop’ service of preference.
Volumes – The scope of ‘first stop’ as opposed to ‘one stop’ will determine the volumes, which are nevertheless likely to be significant
Actors – The number of actors will not be small but nevertheless depends on the scope; for example a first stop shop for e-book availability would involve less actors than a serials Knowledge Base.
Data involved – Depends on the positioning of the service in the discovery ecosystem; compare for example the data held by Google Scholar with that held by multi-faceted services such as Suncat.
Workflows – A constant updating cycle is required, the frequency of which will be dependent on the subject matter; for example, whilst a journals Knowledge Base is less time critical than the Amazon Marketplace, KB suppliers are subject to demands for an improvement on monthly updates.
Current Examples – Google Scholar, Copac, Suncat, Worldcat, serials Knowledge Bases, the Archives Hub and many more currently attempt to provide first stop shops with varying degrees of focus and effectiveness.
Intended Benefits – What is the business case?
Data Owner – Gaining better exposure both to the end user and to machines undertaking such as harvesting
Aggregator – becoming the service of choice opens up a range of value added opportunities, though they need to be clearly scoped relative to the effort of maintaining the service
End User – This model fits with the popular discovery mindset, being a variation of the ‘google it’ paradigm