Over the last several decades, human factors
research has shown repeatedly that the organization of information
in a user interface, or "information architecture," is a critical
factor in usability. An unintuitive organizational structure makes
it difficult for users to find information and impairs their ability
to navigate effectively within the information space. The use
of the information space seems difficult to them and they perceive
the product or website as less useful as well as less easy to
use. Items and functions of key interest to developers and marketers
are often overlooked completely. Consequently, even products which
are usability-tested and attempt to follow other user-centered
design practices can emerge from development with significant
usability flaws.
Problems with a product's information architecture are especially
serious because they are generally difficult to fix after code
development is well underway. A good analogy is that the information
architecture provides the same sort of "floor plan" for a website
that an architect's blueprint provides for a house. It is easy
to change the blueprint before construction begins, but moving
the kitchen from one side of the house to the other after the
plumbing has been put in or deciding to add a second story after
the first story has been framed are generally not economically
feasible changes for the home builder. Similarly, significant
changes to a website's information architecture late in the development
cycle are generally not economically feasible for product developers.
The solution to this problem is to work from the beginning to
make the information architecture fit the way the user thinks
about the information space and available functionality. This
can be accomplished through the use of "mental modeling" techniques,
which are a family of formal methods borrowed from cognitive psychology.
Mental modeling methods make it possible to identify the natural
organizational structure for a product based on data users provide,
prior to beginning user interface design.
Mental modeling techniques must use indirect methods because users
are not able to reliably articulate meaningful relationships among
the elements in a product's information space. ergosoft's
method begins by presenting users with a set of information items
representing the content available in the product's information
space and asking users to make judgments about the relatedness
among the items. The judgment data are then analyzed statistically
to produce a representation of the user's mental model of the
information space from which the items were drawn. The analysis
yields a grouping structure in which conceptually related items
are positioned near one another in the information space and items
that are conceptually unrelated are positioned farther apart.
This mental model forms an information architecture specification
for a product which feels natural to users and is therefore easy
for them to navigate within and use.
An information architecture developed using this method can be
expected to significantly enhance information retrievability,
within-site navigation, and perceived ease of use. Further, ergosoft
has observed in several website usability studies that ease of
navigation also affects the perceived usefulness of a site: users
rate easily navigated sites as more useful as well as easier to
use. Clearly, perceived usefulness is key to repeat visits and
sustained use.
It should be noted that mental modeling studies do not and should
not replace product usability tests. To return to the home building
analogy, mental modeling studies merely provide a way to make
the floor plan more likely to fit the needs of the human inhabitants
of the home without expensive follow-up remodeling projects.
While an intuitive information architecture is a necessary precursor
to good product usability, it is not sufficient by itself. As
implementation choices are made - such as what types of user interface
controls represent what kinds of functions, what terms are used
to name these functions, and what specific interaction sequences
are required of the user - these choices should be usability-tested,
and the results of these usability tests should be fed back to
development to improve the usability of the final product.
The basis of our method is psychological similarity
data that are obtained by having people judge the degree of relatedness
they perceive among a set of items. Relatedness is determined by
having them sort the items into groups based on perceived similarity.
Perceived similarity among items is treated as a measure of psychological
distance.
The data are derived by assigning a value of 0 to every pair of
items that are sorted into the same category. A value of 0 indicates
high similarity and small distance. Every pair of items that are
sorted into different categories is assigned a value of 1, indicating
low similarity and large distance.
The matrices for individual users are summed to produce a group
distance matrix. The largest value occurs if no user ever grouped
a given pair of items together; this maximum value is equal to the
number of users in the sample. The smallest value (0) occurs if
all users grouped a given pair of items together.
The group distance matrix is submitted to cluster analysis, which
computes distances among all the items based on the similarity data
and links the two most similar items together to form a single item
or cluster. It then recomputes the distances and continues linking
the two most similar items at each stage of the process until all
items have been grouped into a single cluster. The distances at
which items are linked at each stage of the clustering process are
examined to identify the point at which they begin to grow notably
large. This indicates the point at which clusters have become conceptually
dissimilar to one another. The number and composition of the clusters
present at this point are optimal for consistency with distinctions
that are psychologically meaningful to users.
The end result is a set of clusters that partitions the information
space into conceptually distinct units. The items within each cluster
(or unit) are optimally conceptually similar, but the clusters are
optimally conceptually different from one another.
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ergosoft
laboratories ©2001-2003
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