In order to design better search experiences, we need to understand the complexities of human information-seeking behaviour. In previous work [13], we proposed a model of information behavior based on an analysis of the information needs of knowledge workers within an enterprise search context. In this paper, we extend this work to the site search context, examining the needs and behaviours of users of consumeroriented websites and search applications. We found that site search users presented significantly different information needs to those of enterprise search, implying some key differences in the information behaviours required to satisfy those needs. In particular, the site search users focused more on simple “lookup” activities, contrasting with the more complex, problem-solving behaviours associated with enterprise search. We also found repeating patterns or 'chains' of search behaviour in the site search context, but in contrast to the previous study these were shorter and less complex. These patterns can be used as a framework for understanding information seeking behaviour that can be adopted by other researchers who want to take a 'needs first' approach to understanding information behaviour.
Classic IR (information retrieval) is predicated on the notion of
users searching for information in order to satisfy a particular
'information need'. However, it is now accepted that much of what
we recognize as search behaviour is often not informational per
se. For example, Broder [
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In this paper, we examine the needs and behaviours of individuals
across a range of site search scenarios. These are based on an
analysis of user needs derived from a series of customer
engagements involving the development of customised site search
applications. In so doing, we extend and validate a model of
information behaviours derived from a previous study of
enterprise search users [
The model is based on a set of ‘search modes’ that users employ
to satisfy their information search and discovery goals. It extends
the IR concept of information-seeking to embrace a broader
notion of discovery-oriented problem solving, addressing a wider
range of information interaction and information use behaviours.
The overall structure of the model reflects Marchionini’s [
We investigate the degree to which the model extends to accommodate the domain of site search (i.e. consumer-oriented websites and search applications) and discuss some of the differences between the needs and goals of enterprise search users versus those of site search. We conclude by exploring the ways in which these modes combine to form distinct chains or patterns, and reflect on the value this offers as a framework for expressing complex patterns of behaviour.
The framework investigated in this study is influenced by a
number of existing models. For example, Bates [
Ellis and his colleagues [
In addition to the research-oriented models outlined above, we
should also consider practitioner-oriented views. Spencer [
In this paper, we use the characteristics of the models above as a
lens to interpret the behaviours found in a new source of empirical
site search data. We also explore the combinatorial nature of the
modes, extending Ellis’s [
The primary source of data in this study is a set of 277 information needs captured during client engagements involving the development of a number of custom site search applications. These information needs take the form of ‘micro-scenarios’, i.e. a brief narrative that illustrates the end user’s goal and the primary task or action they take to achieve it, for example:
The scenarios were collected as part of a series of requirements
workshops involving stakeholders and customer-facing staff from
the respective client organisations. They were generated by
participants in individual breakout sessions, and then 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. [
Moreover, the scope and focus of these scenarios represents a
further point of differentiation. In previous studies, (e.g. [
These scenarios were analyzed using the model derived previously
for the domain of enterprise search [
Although the original study involved three separate analysts, the
behaviours this time were identified by the first author alone. The
current analysis approach is therefore much more subjective.
However, the first author was also the facilitator at each of the
requirements workshops at which the scenarios were generated,
and was able to again a deep insight into the needs, goals and
motivations of the participants. This allowed him to be as
confident as possible in his understanding of the users’
information needs and consistent in his interpretation of the
information behaviours required to satisfy a particular need.
A number of the scenarios focused on needs that did not involve
any explicit information seeking or use behaviour, e.g. “Achieve a
good price for my current car”. These were excluded from the
analysis. A further number were incomplete or ambiguous, or
were essentially feature requests (e.g. “Have flexible navigation
within the page”), and were also excluded. This process resulted
in further confirmation and validation of the nine search modes
identified in the original study, but with revised definitions to
reflect a broader scope:
1. Locate: To find a specific (possibly known) item, e.g. “Find my
reading list items quickly”. This mode encapsulates the
stereotypical ‘findability’ task that is so commonly associated
with site search, consistent with (but a superset of) Spencer’s [
knowledge discovery, e.g. “Find useful stuff on my subject topic”.
In some ways the boundaries of this mode are somewhat less
prescribed than the others, but what the instances share is the
characteristic of open ended, opportunistic search and browsing in
the spirit of O’Day and Jeffries [
Applying the modes described above provides a framework for
understanding the needs of site search users, and an insight into
their likely behaviours. But as with the previous study [
Just as new definitions were needed to accommodate the new domain, new patterns of occurrence were identified in the data. Typically these consisted of chains of length two or three, of which the following were most frequent: 1. 2. 3.
Comprehend): This patterns represents an exploratory search for insight to resolve an explicit information need, e.g. “Assess the proper market value for my car” (45 instances) Opportunity-driven search: (Explore-LocateEvaluate): In contrast to the explicit focus of the pattern above, this sequence represents a less directed exploration in the prospect of serendipitous discovery e.g. “Find useful stuff on my subject topic”(31 instances)
represents a variant of the stereotypical findability task in which some element of immediate verification is required, e.g. “Find trucks that I am eligible to drive” (29 instances) A deeper insight into these patterns can be obtained by presenting them in diagrammatic form, as a network (Figure 1). This diagram illustrates the three sequences outlined above plus other commonly found patterns. It also reflects an outcome of the pervious study, in that certain modes tend to function as “terminal” nodes, i.e. entry points or exit points to a scenario. For example, Explore typically functions as an opening, while Comprehend and Evaluate function in closing a scenario. Analyze typically appears as a bridge between an opening and closing mode.
The sequences described above also allow us to reflect on some of the differences between the needs of site search users and those of enterprise search. One of the most fundamental differences is an emphasis on simpler “lookup” modes such as Locate and Verify: these were relatively rare in the enterprise search data, but prominent in site search (120 and 39 instances respectively). Enterprise search, by contrast, emphasised higher-level “investigate” behaviours such as Analyze and Evaluate (modes which also appeared frequently in site search, but not as prominently: 58 and 61 instances respectively). However, in neither case was the stereotype of ‘search as findability’ borne out: even in site search (where it was the most common mode), Locate was accountable for no more than a quarter of all instances.
But perhaps the biggest difference was in the composition of the chains: while enterprise search was characterised by a wide variety of heterogeneous chains, site searched focused on a small number of common trigrams and bigrams. Moreover, these chains displayed little evidence of the composite nature observed in enterprise search, in which certain chains were seen to be embedded within others to create larger, more complex sequences of behaviour.
A key feature of the current model is its emphasis on the combinatorial nature of search modes, and the value this offers as a framework for expressing complex patterns of behaviour. Such an approach is not unique: the second author, for example, has also previously explored the concept of mode chains to describe information seeking behaviours observed in naturalistic settings. However, his approach was based on the analysis of complex tasks observed in real time, and as such was less effective in revealing consistent patterns of atomic behaviour such as those found in the current study.
Conversely, this virtue can also be a shortcoming: the fact that simple repeating patterns can be extracted from the data may be as much an artefact of the medium as it is of the information needs it contains. These scenarios were expressly designed to be a concise, self-contained deliverable in their own right, and applied as a simple but effective tool in the planning and prioritisation of software development activities. This places a limit on the length and sophistication of the information needs they encapsulate, and hence a natural boundary on the scope and extent of the patterns they represent. Their format also allows the analyst to apply perhaps an unrealistic degree of top-down judgement and iteration in aligning the relative granularity of the information needs to existing modes; a benefit that is less readily available to those whose approach involves real-time, observational data. A further caveat is that in order to progress from understanding an information need to identifying the information behaviors required to satisfy those needs, it is necessary to speculate on the behaviours that a user might perform when undertaking a task to satisfy the need. It may transpire that users actually perform different behaviours which achieve the same end, or perform the expected behavior but through a combination of other nested behaviours, or may simply satisfy the need in a way that had not been envisaged at all.
Finally, the process of inferring information behaviour from selfreported needs can never be wholly deterministic, regardless of the consistency measures discussed earlier. In this respect, further steps should be taken to operationalize the application of the framework and apply some independent measure of stability or objectivity in its usage. driven approach to eliciting user needs, and identified some key differences in user behaviour between the two domains. In addition, we have demonstrated the value of the model as a framework for expressing complex patterns of behaviour, extending the IR concept of information-seeking to embrace a broader range of composite information interaction and use behaviours. Moreover, we propose that our method can be adopted by other researchers who want to take a ‘needs first’ approach to understanding information behaviour.