A language for search and discovery by Tony Russell-Rose.
In order to design better search experiences, we need to understand the complexities of human information-seeking behaviour. In this paper, we propose a model of information behaviour based on the needs of users across a range of search and discovery scenarios. The model consists of a set of modes that users employ to satisfy their information goals.
We discuss how these modes relate to existing models of human information seeking behaviour, and identify areas where they differ. We then examine how they can be applied in the design of interactive systems, and present examples where individual modes have been implemented in interesting or novel ways. Finally, we consider the ways in which modes combine to form distinct chains or patterns of behaviour, and explore the use of such patterns both as an analytical tool for understanding information behaviour and as a generative tool for designing search and discovery experiences.
Tony’s post is also available as a pdf file.
A deeply interesting paper but consider the evidence that underlies it:
The scenarios were collected as part of a series of requirements workshops involving stakeholders and customer-facing staff from various client organisations. A proportion of these engagements focused on consumer-oriented site search applications (resulting in 277 scenarios) and the remainder on enterprise search applications (104 scenarios).
The scenarios were generated by participants in breakout sessions and subsequently moderated by the workshop facilitator in a group session to maximise consistency and minimise redundancy or ambiguity. They were also prioritised by the group to identify those that represented the highest value both to the end user and to the client organisation.
This data possesses a number of unique properties. In previous studies of information seeking behaviour (e.g. , ), the primary source of data has traditionally been interview transcripts that provide an indirect, verbal account of end user information behaviours. By contrast, the current data source represents a self-reported account of information needs, generated directly by end users (although a proportion were captured via proxy, e.g. through customer facing staff speaking on behalf of the end users). This change of perspective means that instead of using information behaviours to infer information needs and design insights, we can adopt the converse approach and use the stated needs to infer information behaviours and the interactions required to support them.
Moreover, the scope and focus of these scenarios represents a further point of differentiation. In previous studies, (e.g. ), measures have been taken to address the limitations of using interview data by combining it with direct observation of information seeking behaviour in naturalistic settings. However, the behaviours that this approach reveals are still bounded by the functionality currently offered by existing systems and working practices, and as such do not reflect the full range of aspirational or unmet user needs encompassed by the data in this study.
Finally, the data is unique in that is constitutes a genuine practitioner-oriented deliverable, generated expressly for the purpose of designing and delivering commercial search applications. As such, it reflects a degree of realism and authenticity that interview data or other research-based interventions might struggle to replicate.
It’s not a bad thing to use data from commercial engagements for research and is certainly better than usability studies based on 10 to 12 undergraduates, two of whom did not complete the study.
However, I would be very careful about trying to generalize from a self-selected group even for commercial search, much less the fuller diversity of other search scenarios.
On the other hand, the care with which the data was analyzed makes it an excellent data point against which to compare other data points, hopefully with more diverse populations.