1. The search box

One of the fundamental concepts in HCI is notion of affordance: the idea that objects should behave in the manner that their appearance suggests. A push plate on a door affords pushing; a handle afford pulling. How many times have you walked up to a door and found it behaved contrary to your expectations? Invariably this is caused by a mismatch between form and function.

Likewise, the design of the search box should follow its function. If its purpose is to allow the user to enter queries in the form of keywords, then it should look like it will accept textual input, and have an associated button which clearly indicates its function. It should also be wide enough to comfortably accommodate the majority of queries:

A match between form and function at eBags.com

The examples below, by contrast, are perhaps somewhat less effective:

Somewhat contrived search box designs

The concept of affordance is so fundamental that it should apply universally, across all types of search context and application. However, the major web search engines choose to differentiate themselves through distinct design treatments.  Google, for example, uses two buttons on its home page, including the somewhat quirky “I’m Feeling Lucky” option (which takes the user directly to the highest ranked page for the current query). Both of these buttons are centre-justified beneath the text box and given a minimal border which only loosely suggests their function:

Minimalist search box design on the Google home page

However, these design choices are perhaps now so familiar to users that they have become accepted as simply a further expression of the Google brand. Note that the positioning and layout of the search box on the homepage is ephemeral anyway; as soon as the first character is entered the page layout changes to accommodate the results and the search box re-locates to the top left of the page. The search button loses its label and gains a ‘looking glass’ icon, which over recent years has become accepted as communicating a search function:

A more conventional search box design on the Google results page

Bing behaves likewise, offering a similar centre-justified search box on the home page, which also relocates to top left once the query has been submitted:

Search box on the Bing results page

Yahoo offers perhaps the most conventional treatment, employing a simple layout and button affordance, both of which remain consistent from home page to search results:

Search box on the Yahoo results page

All three sites assist the user by placing the cursor within the search box upon page load and allowing the user to press the ‘enter’ or ‘return’ key to submit the query. In addition, they reserve a consistent location for the search box, although only Bing and Yahoo display the search box at both the top and bottom of the page.

They also display the query in the search box after submission, which serves as confirmation to the user of what they entered. This may of course differ from how the query is actually interpreted, particularly if an auto-correct or ‘did-you-mean’ is applied. Retaining the query in the search box also provides a convenient starting point for query reformulation (which we’ll cover in a future post).

On the web, users can search for almost anything, with few constraints over topic or medium. By contrast, in site search (i.e. search of a specific web site), the choices are usually much more constrained. This presents an opportunity to provide further direction in the form of “placeholder” text and other prompts to help users construct meaningful queries. People search site Pipl, for example, informs its users that they can search by name & location, email, username or phone number. Note that this text disappears as soon as the search box receives focus:

Pipl search box guides users toward meaningful queries

2. Scoped search

In some search applications the content is assigned to one or more topical categories. For example, items available on eBay are categorised according to eBay’s content taxonomy. This presents an opportunity to allow the searcher to restrict their search scope to a specific category, e.g. using a drop down menu or similar:

Search can be restricted to a specific category on eBay

Inviting the user to select a category in advance helps them narrow down their search more rapidly, and enables the refinement options shown with the search results to be tailored specifically to that product category. For example, a query for ‘golf’ in Cars, Motorcycles and Vehicles would present a very different set of refinement options than the same query in Sporting Goods (see the earlier posts on faceted search). Users with high domain expertise can therefore benefit greatly from scoped search, particularly if they are seeking known items.

Conversely, this approach is less well suited to users with low domain expertise, as they may be unsure which category to select at the outset (unless they take the time to learn and understand the site’s category structure). A poor choice can lead to them over-constraining their search which increases the likelihood of zero results and reduces the potential for serendipitous discovery. Classified advert site Craigslist, for example, offers several category choices – but which one would you choose to find focus group opportunities?

Scoped search at classified advertisement site Craigslist

It turns out the correct answer is under ‘et cetera jobs’ or ‘gigs’. For this reason, scoped search is usually set to ‘all categories’ by default.

The problem is further compounded if the scope restriction is applied to subsequent queries which are unrelated to the original information need. As we saw in previous articles, search is a dynamic process in which the results of one query can change the immediate goal (or even the work task itself). In cases such as these, it is prudent to apply a fall back strategy that searches across all categories, particularly if searching within one category produces zero results. For example, a search on WARC for “text analytics” produces zero results for Charts, but the same query could have been productively applied to ‘All Categories”. In all cases (but particularly those for which zero results are returned), it is important to clearly display the scope of the search as part of the results.

Scoped search could include a fall back strategy at WARC

3. Search within

It is common to think of the search box as the ‘gateway’ to the search experience; the most evident way to initiate an information seeking episode. But there are many cases when keyword search can be productively applied later in the information journey. By allowing users to search within an existing set of results, the query acts as a kind of refinement, narrowing down the results in a manner similar to that of faceted navigation.

For this reason, search within is often presented as a dedicated search box within the faceted navigation menu. Since there are now two separate search boxes on the page, it is necessary to clearly indicate the function of each through the use of placeholder text and other textual labels. In addition, since the keywords are applied as refinements to the current navigational context they should also appear as mementos in the breadbox:

Search within is part of the faceted navigation menu at dabs.com

Alternatively, search within can be integrated with the standard search box, using a radio button or checkbox to toggle between the two different types of input. In such cases, the toggle control needs to be sensitive to the application context (i.e. it should be disabled if search within results is not currently possible). In addition, selecting the ‘search within’ checkbox should also remove the current query from the search box (since it is redundant within the current result set).

Search within is invoked using a checkbox at bulbs.com

Since search within offers the user the ability to enter ad hoc refinements which may not match the current result set, it is quite possible that zero results may be returned. Although this outcome is generally best avoided, there are various techniques for dealing with it productively (which we’ll cover in a future post).

4. Advanced search

In principle, the idea of advanced search is to offer search functionality that goes beyond that implied by the basic search box or the ‘standard’ search experience. By convention, advanced search is usually invoked through a link adjacent to the regular search box:

Advanced search is available via a link at WARC

In the days of parametric search, when the interaction was based around the notion of selecting parameters from an ever-growing form, it may have made sense to withhold some of these choices from the default view and present them instead as an advanced option:

Advanced search form-filling at WARC

But now, with a greater understanding of search as a dynamic, progressive dialogue, there seems less value in adopting an approach that requires the user to make such a commitment in advance of even the first exchange. If you were about to initiate a conversation with a stranger, would you ask that they to choose a tone of voice first (e.g. ‘normal’ or ‘sophisticated’)? A far more productive strategy would be to demand the minimum at the outset, then modify your interaction as the discourse unfolds, reacting and responding meaningfully to each exchange.

Of course, there will always be applications for which it makes sense to divide the audience into two or more groups, such as medical information sites that serve both clinical professionals and the public. But in such cases, a more informed strategy may be to consider how the whole experience (i.e. content, navigation, transactional functionality, etc.) could be adapted for that audience, rather than assume that search alone deserves special treatment. An effective search experience puts ‘advanced’ search tools in the hands of all users, as and when they are able and willing to use them. In practice, many of the instances of ‘advanced search’ as described above have transpired to be either unnecessary, underutilized, or both. We touched on this issue previously, when we reviewed the ways in which faceted search can provide a more elegant and scalable approach to advanced search.

5. Beyond keywords

In the examples above we’ve looked at a number of ways we can search using various forms of typed input. But entering keywords isn’t the only way to express a query. In fact, there are host of other ways we can articulate an information need.

5.1 Natural Language

One of the most intuitive is to express the query as you would to another human being, i.e. as a natural language question or request. This kind of interaction was popularised by search engines such as Ask (formerly Ask Jeeves), which uses a combination of text analytics and human moderation to produce a question-answering search experience:

Natural language question answering at Ask

In fact, natural language has often been portrayed as the “killer app” for search, prompting the creation over numerous start-ups over the last decade or more. However, up until a few years ago, disappointingly few of these had had a lasting effect on the mainstream search experience. This is partially due to the inherent challenge of developing robust algorithms for natural language processing (NLP). But it also reflects the dynamics of the search experience itself: to effectively support human information seeking across the widest range of task contexts, we need to facilitate an open, scalable and interactive dialogue. Answering questions may be part of this, but it is not the whole solution. For some types of application, techniques such as faceted search can facilitate the search conversation in a more transparent fashion than an exchange of purely linguistic constructs.

But that isn’t to say NLP has no future in search. On the contrary, it just needs to be applied in the right manner. For example, NLP techniques are currently being applied to an ever growing variety of chatbots and interactive agents to provide customer service and other types of automated support across a wide range of industries and domains. And at True Knowledge, NLP is used to provide a question answering service that determines the meaning of questions which it then matches against discrete facts in its database. Likewise, Wolfram Alpha uses NLP to answer factual queries by computing answers and relevant visualizations from a knowledge base of curated, structured data:

Natural language question answering at Wolfram Alpha

5.2 Non-text queries

Information needs don’t have to be expressed exclusively in linguistic form. Sometimes a visual medium can be more natural, particularly if an example already exists. Google, for example, allows users to drag & drop an image to use as a search query:

Search by example using images at Google

Similarly, Like.com (now part of Google Product Search) allowed users to use images to describe parts of queries which would have been difficult to describe using keywords alone.  And beyond visual queries, services like Shazam allow users to record music clips which it then identifies by matching them against a database of audio files.

Each of these services represents a type of search known as query by example. But queries don’t have to be complete samples. Retrievr, for example, allows users to search by sketching a shape or outline. And Etsy allows user to explore by selecting colours from a palette:

Search by colour at Etsy

Figure X:Search by colour at Etsy

These alternative forms of input serve to remind us that keywords may be the simplest form of input, but they are not always the most natural. Sometimes our information needs go beyond words. Instead, we should choose input methods that match the broader information landscape.

6. Summary and best practices

The search box

• Form should follow function: apply the principles of affordance to interactive design elements
• Reserve a consistent location for the search box, and make it wide enough to comfortably accommodate the majority of queries
• Place the cursor within the search box upon page load and allow the user to press the ‘enter’ or ‘return’ key to submit the query
• Provide direction in the form of “placeholder” text and other prompts to help users construct meaningful queries (remove this as soon as the search box receives focus)
• Display the query in the search box after submission

Scoped search

• Consider scoped search for applications where users have high domain expertise (but avoid forcing this on users with low domain expertise). Ensure that it defaults to “all categories”
• Apply a fall back strategy that searches across all categories if searching within one category produces zero results
• Clearly display the scope of the search as part of the results

Search within

• If presented as part of a faceted menu, clearly indicate the function through the use of placeholder text and other textual labels. Ensure that keyword refinements appear as mementos in the breadbox
• If presented as an option to the global search box, ensure that the toggle control is sensitive to the application context. In addition, selecting the ‘search within’ checkbox should remove the current query from the search box

Advanced search

• Review the rationale for advanced search: in particular whether it is better to customise the whole experience (i.e. content, navigation, transactional functionality, etc.) for a specialist audience, rather than assume that search alone deserves special treatment

Beyond keywords

• Sometimes our information needs go beyond words: choose input methods that match the medium

Related Posts:

1. A Task-based Model of Search
2. A Taxonomy of Enterprise Search and Discovery
3. A Taxonomy of Search Strategies and their Design Implications
4. Designing Faceted Search: Getting the basics right (part 1)
5. From Search to Icon Design: a grammar for visual communication

And while we’re on a general Eats, Shoots & Leaves theme, since when did “likely” become an adverb? Continued use of this word in such a manner will ‘likely’ annoy me. Sadly, proliferation of this grammatical monstrosity makes truly depressing viewing (according to Google ngrams):

And since when did the phrase “next several” become anything less than a complete abomination? What’s wrong with “next few”? This is yet another thing that’ll ‘likely’ annoy me for the ‘next several’ years:

And as for ‘submittal’ (and the even more abhorrent ‘transmittal’, with its equally pointless cousin “expiration”) if anyone commits these atrocities again I will beat them into ‘submittal’ with a leatherbound copy of the OED.

Related Posts:

1. Text Analytics for Medical Informatics + Question Answering
2. Prostitutes Appeal to Pope: Text Analytics applied to Search
3. The role of Natural Language Processing in Information Retrieval
4. Applying text analytics to product innovation and legal cases
5. Text Analytics: Yesterday, Today and Tomorrow

Here’s an excerpt:

The concert hall at the Sydney Opera House holds 2,700 people. This blog was viewed about 31,000 times in 2011. If it were a concert at Sydney Opera House, it would take about 11 sold-out performances for that many people to see it.

Click here to see the complete report.

Related Posts:

1. It’s Tony, as in ‘P-e-t-e’
2. No Social Media please, we’re Ergonomists
3. Welcome to Information Interaction

Now, I’m not the first person to have made this observation (and I doubt I’ll be the last), but it occurs to me that one of the reasons the debate exists in the first place is that the community lacks a shared vocabulary for defining these concepts, and when we each talk about “search tasks” we may actually be referring to quite different things. So to clarify how I see the landscape, I’ve put together the short piece below. More importantly, I’ve tried to connect the conceptual (aka academic) material to current design practice, so that we can see what difference it might make if we had a shared perspective on these things. As always, comments & feedback welcome.

A Task-based Model of Search

In A Taxonomy of Enterprise Search we reviewed various models of information seeking, from an early focus on queries and documents through to a more contemporary notion of search as an information journey driven by dynamic information needs. Continuing this thread of moving from the ‘micro’ to the ‘macro’ level leads us, inevitably, to context.

In this article, we apply a task-based lens, examining the various layers of context that influence the search process. But to understand the effects of these influences in a principled manner, we need first to establish a framework and vocabulary for the key concepts and their relationships. The graphic below presents such a framework, based on the work of Jarvelin and Ingwersen (2004).

A task-based model of search

This model represents the multiple levels of task context as a set of layers and the criteria by which they are evaluated. These start from the micro level (the ‘information retrieval context’) and extend outwards to the macro level (the ‘socio-organizational and cultural context’).

The Information Retrieval Layer

At the most granular level (the innermost layer in the figure), we have information retrieval.  This layer is typified by simple, focused tasks such as finding a specific document or resource related to a keyword query. An example might be a shopper searching an online bookstore for the latest Harry Potter book, or an engineer searching a parts database for a component with the serial number 123-456. These tasks are often referred to as known item searches. They may involve a number of iterations, but are usually confined to a single session. The success of tasks at this level is commonly evaluated using system-oriented metrics such as precision and recall.

The Information Seeking Layer

At the next level, we have information seeking. This layer is associated with broader, more complex tasks that attempt to satisfy a perceived information need or problem (Marchionini, 1995). An example might be a shopper trying to find shoes to match their interview suit, or an engineer trying to find components that are compatible with a particular product design.

At this level, users need to exercise judgment regarding which strategies to adopt, such as where, how and when to look for information (Wilson et al, 2010). This can include determining which particular repositories to search and which approaches to adopt. For example, they may choose to browse, enter a keyword query, or to apply some combination of the two approaches. Users may find themselves performing a series of information retrieval tasks as part of a broader information-seeking session. The success of tasks at this level is usually evaluated by assessing the quality of information acquired relative to the information need.

The Work Task Layer

The information need that motivates information seeking is itself motivated by a further level of search: the work task. This layer is characterized by higher-level tasks that are created when the user recognizes an information need based on either an organizational need or personal motive (Marchionini, 1995). An example of an organizational need might be an engineer trying to understand product lifecycles and manage the risks associated with component obsolescence. An example of a personal motive, on the other hand, might be a shopper who wants to understand the available options in selecting an affordable home entertainment system for their family.

Work tasks are situated in an organizational setting and are likely to reflect local resources, constraints and working practices. This can include which particular approaches may be used to satisfy a given information need, and what resources are available, e.g. reference materials, libraries, human experts, etc. (Wilson et al, 2010). Evaluation at this level commonly focuses on assessing performance of the overall task. For the engineer mentioned above, this could mean developing product designs that use parts from preferred suppliers involving a minimal risk of obsolescence.

The Socio-organizational and Cultural Context Layer

Finally, we have the highest level in the model: the socio-organizational and cultural context. This level influences not only the overall task requirements but also the collective importance attached to meeting them. For example, the expectations associated with completing a given work task may be perceived differently when considered within the context of a large public sector organization, a small start up business, or a home-based hobby.

In the remainder of this article, we’ll be primarily considering the first three levels of this model: information retrieval, information seeking, and work task.

Designing across Layers

The model above provides a useful lens through which to view the various layers of the search task. But more importantly, it provides a framework for understanding what type of design support is most likely to be effective at each level.

To illustrate, let’s return to our shopper who is trying to understand the various options in selecting an affordable home entertainment system. The overall goal is driven by a personal motive at the work task level, but in satisfying this goal, they will need to undertake a number of sub-tasks across several layers of the task context. We can examine the effect of context at each level, and explore what kinds of design support are appropriate. We can also start to think about search as a series of sub-tasks, reflecting the stages in the information seeking process.

At the outset, they are likely to be constrained by a lack of domain knowledge (e.g. of the main product types), and may be unsure of what questions to ask or even where to ask them. Perhaps they start by searching the website of an electrical retailer such as Comet. Unfortunately, tasks at this level are often poorly supported by online retailers, and a query for “home entertainment” returns an opaque list of product categories, which relies on the user knowing the terminology and which category to select.

Limited support for the work task context at Comet

But behind the tab labeled “Videos and Advice” lies a resource which is much more appropriate for this level of task. Instead of product categories, a query for “home entertainment” on PluggedIn returns content much better suited to goals at the work task level. These include tutorial information in the form of “buyer’s guides” and “how to guides” alongside product reviews and user generated content from topical forums and commentary streams. In contrast to the product category listing seen previously, this material provides far greater support for activities associated with the work task level, such as exploration and learning. In addition, it supports serendipitous discovery of latent needs through the provision of inspirational articles and expert reviews.

Support for the work task context at PluggedIn

After exploring and reviewing this material, the user may start to formulate a more specific idea of the options open to them and the various tradeoffs involved. In so doing, the focus shifts from the higher-level work task to a set of information-seeking sub-tasks associated with those specific options. As their understanding deepens, they may wish to start collecting a list of ideas or candidates to investigate in greater detail at a later date. What kind of support can be provided to facilitate this? A simple but effective example can be seen at Amazon, which supports iterative information seeking via a personalized history panel that includes recent searches and recently viewed items. This is augmented by a facility for users to create, organize, and share their own lists.

Support for the information seeking context at Amazon

As the user starts to get a clearer idea of their needs and the most appropriate products, they may wish to review and verify the details of these particular items on independent sites. In this context, the focus shifts from information seeking to a set of specific information retrieval sub-tasks. This is the level that traditionally has been best supported by online retailers, and there are many examples of design support for such tasks. One notable example can be found at Samsung, which supports search via keyword queries using a particularly immersive style of auto-suggest.

Support for the information retrieval context at Samsung

This facility helps users not only to accurately enter valid product names and details, but also provides character-by-character interactive guidance through the product suggestions shown in the dialog overlay.

Summary

Search tasks are defined by three primary levels: information retrieval, information seeking and work task. Each of these layers provides a unique lens through which to view the search experience and understand the types of design support that are appropriate at each level. In a future article, we’ll build on our understanding of the task context to explore the physical context in greater depth, examining the fundamental influences that guide and shape the mobile search experience.

References

1. Ann Blandford and Simon Attfield (2010). ‘Interacting with Information’, Morgan & Claypool 2010
2. Kalervo Jarvelin and Peter Ingwersen (2004). “Information seeking research needs extension towards tasks and technology”, Information Research, Vol. 10, No. 1. (October 2004)
3. Gary Marchionini, (1995). Information Seeking in Electronic Environments. Cambridge University Press.
4. Max L. Wilson, Bill Kules, m.c. schraefel and Ben Shneiderman (2010) “From Keyword Search to Exploration: Designing Future Search Interfaces for the Web”, Foundations and Trends® in Web Science: Vol. 2: No 1, pp 1-97.

Related Posts:

1. Findability is just So Last Year
2. A Taxonomy of Enterprise Search and Discovery
3. User Interface Design Patterns for Search & Information Discovery
4. A Taxonomy of Search Strategies and their Design Implications
5. Patterns of Personalization

To test it we applied it to a few notable instances to see where they would fit. The outcome revealed personalization types that could exist in theory, but don’t in practice – which of course starts you wondering why. I should point out that the ‘research’ behind this was relatively modest, so it’s by no means the final word, but it’s interesting food for thought (until I get round to writing a proper piece on ‘advanced search’

Related Posts:

1. From Search to Icon Design: a grammar for visual communication
2. A Taxonomy of Enterprise Search
3. Design Patterns for Spatial Information Visualisation and Analytics Applications
4. User Interface Design Patterns for Search & Information Discovery
5. Search UI Design Patterns

Looks like the appetite for changing ownership has really taken root at Endeca. Casually perusing one of my old blog posts on Search Facets, I was amazed to see the byline spelled ‘P-e-t-e B-e-l-l’. Now, I’ve seen some weird auto-corrections in my time, but that’s quite some typo. You just can’t trust those spell checkers, can you.

Related Posts:

1. The Dimensions of Search User Experience
2. Findability is just So Last Year
3. Designing the Search Experience (tutorial at Search Solutions 2011)
4. A Taxonomy of Search Strategies and their Design Implications
5. Interaction Models for Faceted Search

One of the great things about membership of the IEHF (Institute of Ergonomics and Human Factors) is the support it provides for professionalism in the workplace, backed up by a strong sense of tradition underpinning the academic roots of the discipline. But perhaps one of its weaker points is our communal readiness to embrace new ways of working and communicating, particularly regarding social media and other online networking tools (e.g. blogs, wikis, Twitter, LinkedIn, etc). There seems to be a perception (among the wider UX community, at least) that the IEHF is somewhat backward looking in this respect, with one ex-member describing it as “another association that’s extremely old school and slooooooooooow”. Reading some of these comments, you’d think that the average IEHF member couldn’t tell their RSS from their elbow. But is this really true? Is the footprint of the IEHF across social media really that shallow, or do we in fact have a small army of nascent bloggers and tweeters? I raised the issue recently on the LinkedIn IEHF forum, and this brief article summarises the response.

First, let’s look at blogs by people in the IEHF community whom many of us will know: Council members (both current and recent). Credit here goes to Tom Stewart, who features regularly as a guest blogger on Econsultancy, and thus earns double kudos for (a) writing engaging, topical content and (b) reaching out to ‘non ergonomic’ communities. On a personal note, I think this is something we should be doing a lot more of: there are plenty of folk out there in the digital community who would be very receptive to the IEHF’s message if only it was articulated in terms that resonate with them.

Then there’s Ed Chandler, whose UserAccess Design blog covers “Usability and Accessibility in the Information Society”. Ed is also creator of the UserAccess Daily, an online newspaper on the subject of accessible design. This publication (in common with others from paper.li) is generated automatically from syndicated online sources on a regular schedule. The end result: a newspaper-like reading experience, delivered to your inbox. Nice work, Ed.

These two, combined with my own blog (Information Interaction), means that out of a set of ~19 Council members, we appear to have just three active bloggers. Of course, I recognize that blogging is just one channel through which to engage the wider community, but given our aspirations to grow the membership base and appeal to a more youthful demographic, I had hoped to see more.

Looking further afield, there are blogs by various IEHF members, such as Brian Sherwood Jones’ Usability in Context blog which covers an eclectic but highly accessible mix of articles on the subject of “Usability and Quality in Use in everyday life”. Also highly readable (and well illustrated) is Paul Salkeld’s “Observations of Ergonomics”, which covers a variety of workspace and product ergonomics issues.

Also worth a look are Guy Osmond’s Off at Tangents blog, Roy Matheson’s Philosophy on Workplace Safety and Work Injury Evaluation”, Mark Paradies’ blog on root cause analysis, Andy Brazier’s Human factors in risk management, and Yamile Jackson’s Nurtured by Design which covers neonatal ergonomics.

And then there are the corporate blogs, i.e. those maintained on behalf of a larger organisation by a number of active contributors. Notable among these are the ABB blog, which covers process safety related HF issues, CCD’s Design and the Human Factor, Davis Associates’ User Insights for Design and Brunel University’s Human Centred Design Institute blog.

But there’s more to social media than blogging. If you want to keep up with the Institute on Twitter, for example, you can follow @ukiehf. And Dominic Furniss of UCL is using Twitter as an online diary study tool to gather user data on human error. But beyond that, the number of active IEHF members on Twitter seems modest at best. By contrast, we seem to be doing rather better on LinkedIn, where the IEHF group now has over 1,200 members and is growing at a healthy rate of 50+ new members each month (note however that this also includes non-IEHF members). Likewise, the Institute has an active presence on Facebook, with various related pages for events such as the student conference. Finally, there are of course other popular blogs on the topics of ergonomics, human factors, usability, user experience and so on, but I haven’t included them here unless there is a clear and direct link to the IEHF (e.g. through personal membership).

So what’s my assessment of the IEHF’s engagement with social media? Overall, my impression is one of missed opportunity (thus far). After all, there must be thousands of people worldwide working in user experience design for online products and services, in many cases developing the very sites and tools that provoked this discussion. And yet, we count but a handful of these as active IEHF members. No irony there, then.

Finally, remember also that this is an ongoing effort, open to further contributions, so if I’ve omitted your personal favourite (or your own blog), drop me a line either here or by email.

BTW, from next year I’ll be standing down from my role as Associate Rep, so if you’re interested in picking up the reins from me, get in touch.

Related Posts:

1. Changing terms for changing times: Usability, HCI, UCD & more
2. Ergonomics, Human Factors, User Experience: changing terms for changing times
3. The Ergonomics Society and the Digital Blind Spot
4. User Research Plans for the Ergonomics Society Website
5. User scenarios for Ergonomics Society website

Last Wednesday I had the privilege of co-chairing the 5^th Search Solutions conference, held at BCS London in Covent Garden. As in previous years we had an eclectic mix of presentations, panels and keynote talks by influential industry leaders on novel and emerging applications in search and information retrieval. But new for this year was the tutorial programme: a selection of practical training courses reflecting current topics and state-of-the-art methods in search and information retrieval.

In hindsight, I can confide that this was something of a gamble: the idea of a tutorial programme came to our attention very late, and we had no idea whether we’d attract sufficient attendees (particularly given the modest size of the event compared to, say, ECIR or CIKM). However, in the end both tutorials were fully booked; a strong signal that we should consider a similar arrangement next year (perhaps even with a more extensive programme, e.g. four tutorials rather than two).

So, onto the event itself. Various folks have written summaries of the whole event (e.g. Charlie Hull’s), so I’ll just pick out a few highlights here.

Probably the main one for me was Ricardo Baeza-Yates presentation “Beyond the Ten Blue Links”, which discussed Yahoo’s ongoing quest to satisfy the implicit and explicit needs of web search users, presented as a set of seven “challenges”. Some of these challenges you might have expected, such as Query Assistance (e.g. suggestions, related searches, and so on) and Universal Search (i.e. dealing with mixed media results). But other challenges were more unprecedented, e.g. “Post Search User Experience” and “Application Integration”. Both of these suggest a wider re-framing of the search problem, in which findability is just one (small) part of the overall search experience. In this context, the focus is no longer on low-level activities such as selecting relevant documents, but on recognising and providing support for the completion of higher-level tasks. This is interesting in its own right, but it also underlines Search Solutions policy of bringing together the web and enterprise search communities: in this instance, we clearly can learn a lot from each other.

There are also some fascinating things going on at the British Library, as reported by Lewis Crawford. They’ve certainly taken on ambitious challenge: to provide access and analytics to the UK Web Archive (a kind of British version of the Wayback Machine). Like Yahoo, this talk also addressed the changing nature of users’ search tasks, suggesting that the real value may not be so much in allowing users to ‘find needles in haystacks’, but in providing tools to manipulate and query the haystack itself. Lewis presented an intriguing collection of visualisation and data analysis tools, allowing users to explore and discover patterns and trends in the data, and even build their own applications using their data and APIs.

I should also call out the talk by Ian Kegel, who presented a summary of BT’s work in helping their broadband TV customers find and watch programmes on demand from their extensive catalogues. In this context, search is no longer a goal-directed activity per se, but exists within a wider ecosystem of robust and relevant content recommendations, delivered as part of an ambient, serendipitous pleasurable experience and distributed across multiple devices. Particularly interesting was Ian’s observation that 35% of Amazon’s product sales come from recommendations, underlining just how important it is to get this right.

Charlie Hull, Marianne Sweeny and John Tait share the fishbowl

Finally, I should also mention the Fishbowl session. This was also something of an experiment, but I’m please to say it worked rather well with comment and debate from across the floor, no doubt facilitated by a rather good Merlot (the photo above doesn’t do it justice!)

So that’s it for this year. If you missed it, you’ll be pleased to learn that the presentations are now available online. Next year’s event will be held around the same time (mid-late November), also at BCS London – details to be announced shortly. If you’d like to get involved, e.g. as a speaker or co-organiser, just drop me a line either here or offline. Likewise, if there’s a tutorial topic you’d particularly like to see (or even present), then let us know!

Related Posts:

1. Search Solutions 2011: London, November 16
2. Designing the Search Experience (tutorial at Search Solutions 2011)
3. Search Solutions 2011: lineup announced!
4. Search Solutions 2010: Reactions & Reflections
5. Search Solutions 2010: Topics & Titles

A couple of weeks ago I had the pleasure of presenting a paper at Enterprise Search Europe on a Taxonomy of Enterprise Search. This was the first time that the Enterprise Search Summit had found its way this side of the Atlantic, and I’m pleased to say it was a great success (due in no small part to the efforts the conference chair, Martin White).

The paper was essentially a research-driven piece, reporting on some empirical work into studying the search strategies and tactics that users commonly employ across a range of enterprise search contexts. As such, it mirrors Andrei Broder’s classic 2002 paper (A Taxonomy of Web Search), which addresses a broadly similar goal within the domain of web search. However, we used a more qualitative, user-oriented data source, and also extended the analysis to present some initial implications into how the findings could be applied in the design of search and discovery experiences.

After the event, Martin confided in me how unusual it would be to see such a paper at the New York event, intimating that there would be little room in the program for such a piece. That conversation and a subsequent exchange with Daniel Tunkelang at the CIKM Industry Event got me thinking: is the search industry playing its part in building an effective dialogue between researchers and practitioners? Could it do more? Is the job of disseminating and promoting the benefits and outcomes of IR research purely the responsibility of academics and researchers?

I hope to explore this issue further in a subsequent post. For now, here are the slides from the event. The associated paper is also available in a previous post and as a pdf from the HCIR conference website.

Related Posts:

1. A Taxonomy of Enterprise Search and Discovery
2. Findability is just So Last Year
3. Designing the Search Experience (tutorial at Search Solutions 2011)
4. A Taxonomy of Search Strategies and their Design Implications 
5. Search Solutions 2011: London, November 16

I’ve included the paper below in its entirety. It’s also available in pdf form from the HCIR website.

ABSTRACT

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. Broder (2002) has shown that the need underlying a given web search could in fact be navigational (e.g. to find a particular site) or transactional (e.g. through online shopping, social media, etc.). Similarly, Rose & Levinson (2004) have identified the consumption of online resources as a further common category of search behaviour.

In this paper, we extend this work to the enterprise context, examining the needs and behaviours of individuals across a range of search and discovery scenarios within various types of enterprise. We present an initial taxonomy of “discovery modes”, and discuss some initial implications for the design of more effective search and discovery platforms and tools.

1. INTRODUCTION

To design better search and discovery experiences we must understand the complexities of the human-information seeking process. Numerous theoretical frameworks have been proposed to characterize this complex process, notably the standard model (Sutcliffe & Ennis 1998), the cognitive model (Norman 1988) and the dynamic model (Bates, 1989). In addition, others have investigated search as a strategic process, examining the various strategies and tactics that information seekers employ over extended periods of time (e.g. Kuhlthau, 1991) and the effects of various levels of task context (e.g. Jarvelin and Ingwersen, 2004).

In this paper, we examine the needs and behaviours of varied individuals across a range of search and discovery scenarios within various types of enterprise. These are based on an analysis of scenarios derived from numerous customer engagements involving the development of search and business intelligence solutions based on the Endeca Latitude software platform. In so doing, we extend the classic IR concept of information-seeking to a broader notion of discovery-oriented problem solving, accommodating the much wider range of behaviours required to fulfil the typical goals and objectives of enterprise knowledge workers.

Our approach to enterprise discovery is an activity-centred model inspired by Don Norman’s Activity Centred Design (Norman 2006). This approach is an extension of previous activity-centred modelling efforts which focused on a “captur[ing] a systematic and holistic view of what users need to accomplish when undertaking information retrieval tasks more complex than searching” (Lamantia 2006), employing Grounded Theory to provide methodological structure (Glaser 1967).

In this context, we present a model which has at its core an initial taxonomy of the “discovery modes” that knowledge workers employ to satisfy their information search and discovery goals. We then discuss some initial implications of this model for the design of more effective search and discovery platforms and tools.

2. INFORMATION RETRIEVAL MODELS

The classic model of IR assumes an interaction cycle consisting of four main activities: the identification an information need, the specification of an appropriate query, the examination of retrieval results, and reformulation (where necessary) of the original query. This cycle is then repeated until a suitable result set is found (Salton 1989).

In the above models, the user’s information need is assumed to be static. However, it is now acknowledged that information seekers’ needs often change as they interact with a search system. For example, Bates (1989) proposed the dynamic “berry-picking” model of information seeking, in which the information need (and consequently the query) changes throughout the search process This model also recognises that information needs are not satisfied by a single, final result set, but by the aggregation of results, insights and interactions along the way.

Bates’ work is particularly interesting as it explores the search strategies and tactics that professional information-seekers employ. In particular, Bates identifies a set of 29 individual tactics, organised into four broad categories (Bates, 1979). Likewise, O’Day & Jeffries (1993) examined the use of information search results by clients of professional information intermediaries and identified three distinct categories of search behaviour: (1) Monitoring a known topic or set of variables over time; (2) Following a specific plan for information gathering; (3) Exploring a topic in an undirected fashion. O’Day and Jeffries also observed that a given search would often evolve over time into a series of interconnected searches, delimited by certain triggers and stop conditions that indicate the transitions between modes or individual searches executed as part of an overall enquiry or scenario.

More recently, Cool & Belkin (2002) proposed a faceted classification of interactions with information, in which their Information Behaviors facet contained nine disjunctive activity types (Create, Disseminate, Organize, Preserve, Access, Evaluate, Comprehend, Modify and Use). By contrast, Marchionini (2005) identifies three major categories of search activity (Lookup, Learn and Investigate) while Spencer (2006) suggests four modes of information seeking (Known-item, Exploratory, Don’t know what you need to know, and Re-finding).

3. A TAXONOMY OF ENTERPRISE SEACH AND DISCOVERY

The primary source of data in this study is a set of 104 user scenarios captured during numerous customer engagements involving the development of search and business intelligence solutions based on the Endeca Latitude software platform. These scenarios were collected using a variety of methods, e.g. interviews, stakeholder workshops, direct observation, etc. They take the form of a simple narrative that illustrates the user’s end goal and the primary task or action they take to complete it, followed by a brief description of their job function or role, for example:

• “I need to understand a portfolio’s exposures to assess portfolio-level investment mix” (Portfolio Manager)
• “I need to understand the quality performance of a part and module set in manufacturing and the field so that I can determine if I should replace that part” (Engineering)

These scenarios were manually analyzed to identify themes or modes that appeared consistently throughout the set, using a number of iterations of a ‘propose-classify-refine’ cycle based on that of Rose & Levinson (2004). Inevitably, this process was somewhat subjective, echoing the observations made by Bates (1979) in her work on search tactics:

“While our goal over the long term may be a parsimonious few, highly effective tactics, our goal in the short term should be to uncover as many as we can, as being of potential assistance. Then we can test the tactics and select the good ones. If we go for closure too soon, i.e., seek that parsimonious few prematurely, then we may miss some valuable tactics.”

There are however some guiding principles that we can apply to facilitate convergence on a stable set. For example, an ideal set of modes would exhibit properties such as:

• Consistency (they represent approximately the same level of abstraction)
• Orthogonality (they operate independently to each other)
• Comprehensiveness (they address the full range of discovery scenarios).

An initial set of nine discovery modes emerged from this analysis, which were subsequently grouped according to the three top-level categories proposed by Marchionini (2005). The nine modes are as listed below with a brief definition:

1. Lookup

1a. Locating: To find a specific (possibly known) item;

1b. Verifying: To confirm or substantiate that an item or set of items meets some specific criterion;

1c. Monitoring: To maintain awareness of the status of an item or data set for purposes of management or control.

2. Learn

2a. Comparing: To examine two or more items to identify similarities & differences;

2b. Comprehending: To generate insight by understanding the nature or meaning of an item or data set;

2c. Exploring: To proactively investigate or examine an item or data set for the purpose of serendipitous knowledge discovery.

3. Investigate

3a. Analyzing: To critically examine the detail of an item or data set to identify patterns & relationships;

3b. Evaluating: To use judgment to determine the significance or value of an item or data set with respect to a specific benchmark or model;

3c. Synthesizing: To generate or communicate insight by integrating diverse inputs to create a novel artefact or composite view.

Evidently, this taxonomy has been derived from a single data set and in that respect would benefit from further refinement. For example, Monitoring may be classified as a Lookup activity in the context of an engineer receiving a simple alert message, but it acts more as an Investigate activity when viewed in the context of an executive reviewing an organizational dashboard. Conversely, Exploring is a concept whose level of abstraction seems somewhat higher than the others, potentially compromising the consistency principle suggested above.

However, the true value of the modes will be realised not by their conceptual purity or elegance but by their utility as a design resource. In this respect, they should be judged by the extent to which they facilitate the design process in capturing important characteristics common to enterprise search and discovery experiences, whilst accommodating arbitrary variations in domain, information resources, etc.

4. MODE SEQUENCES AND PATTERNS

A further interesting observation arising from this analysis is that the mapping between scenarios and modes is not one-to–one. Instead, the modes tend to cluster, forming distinct chains or patterns analogous to higher-level syntactic units. More often than not, one particular mode will play a dominant role in the sequence. These patterns provide a framework for understanding the transitions between modes (echoing the triggers identified by O’Day & Jeffries), and can be used be used to provide further insight into enterprise search and discovery behaviour.

These mode chains echo the above-mentioned efforts to create goal-based information retrieval models, which yielded modes and a set of broadly applicable “information retrieval patterns that describe the ways users combine and switch modes to meet goals: Each pattern is assembled from combinations of the same [elemental] modes” (Lamantia 2006).

Figure 1. Discovery mode network

The five most frequent mode patterns are listed below. These have been assigned descriptive (if somewhat informal) labels and an associated example scenario:

1. Comparison-driven optimization: (Analyze-Compare- Evaluate) e.g. “Replace a problematic part with an equivalent or better part without compromising quality and cost”
2. Exploration-driven optimization: (Explore-Analyze-Evaluate) e.g. “Identify opportunities to optimize use of tooling capacity for my commodity/parts”
3. Strategic Insight (Analyze-Comprehend-Evaluate) e.g. “Understand a lead’s underlying positions so that I can assess the quality of the investment opportunity”
4. Strategic Oversight (Monitor-Analyze-Evaluate) e.g. “Monitor & assess commodity status against strategy/plan/target”
5. Comparison-driven Synthesis (Analyze-Compare-Synthesize) e.g. “Analyze and understand consumer-customer-market trends to inform brand strategy & communications plan”

Further insight may be derived by examining how the mode patterns combine across all the scenarios to form a “mode network”, as shown in Figure 1. Evidently, some modes act as “terminal” nodes, i.e. entry points or exit points to a discovery scenario. For example, Monitor and Explore feature only as entry points at the initiation of a scenario, whilst Synthesize and Evaluate feature only as exit points to a scenario.

5. DESIGN PRINCIPLES FOR SEARCH AND DISCOVERY SOLUTIONS

The modes establish a ‘taskonomy’ or collection of defined discovery activities which are structurally consistent, domain independent, orthogonal, semantically distinct, conceptually connected, and flexibly sequenceable.  Such a profile — analogous to notes in the musical scale, or the words and phrases we assemble into sentences — could serve as a language for the design of variable scale discovery solutions through the use of common constructive mechanisms such as concatenation, combination and nesting. And if the modes do act as an elementary grammar for discovery, then sustained use as a functional and interaction design language should result in the creation of larger and more complex units of meaning which offer cumulative value.

Professional experience with employing the modes as both an analytical framework for understanding discovery needs and as a design grammar for the definition of discovery solutions suggests that both implications are valid.  Further, our observations of using the modes suggest the existence of recognizable patterns in the design of discovery solutions. We will briefly discuss some of the patterns observed, doing so at three common levels of solution scale: on the level of a single functional or interface element, for whole screens or interfaces composed of multiple functional elements, and for applications comprising multiple screens.

5.1 Single element patterns

5.1.1 Comparison Views

One of the most common design patterns is to support the need for the Compare mode by creating A/B type comparison views that present two display panes – each containing data display charts or tables; or single items or groups of items – side by side to emphasize similarities and differences.

5.1.2 Contextual Views

Another common design pattern supports the Analysis mode by allowing a fore-grounded view of a single chart, table, item, or list, accompanied by its contextual ‘halo’ – the full body of information available about the element such as status, origin, format, relationships to other elements; annotations; etc.

5.2 Whole screen patterns

5.2.1 Dashboard

One of the most common screen-level design patterns is to support the Monitoring and Synthesis modes by presenting a collection of metrics which in aggregate provide the status of independent processes, groups, or progress versus goals in a ‘dashboard’ style screen.

5.2.2 Visual Discovery Screen: 4-Dimensions

A second common screen-level design pattern for discovery experiences is the visual discovery screen, which supports modes such Exploration, Evaluation, and Verification by layering views that present visualizations of several dimensions of a single axis of focus such as a core process, organizational unit, or KPI. When switching between layered views, the axis in focus remains the same, but the data and presentation in the dimensions adjusts to match the preferred discovery mode.

5.3 Application-level patterns

5.3.1 Differentiated Application

The ‘Differentiated Application’ pattern assembles a collection of individual screens whose distinct compositions and designs support individual discovery modes of Analysis, Comparison, Evaluation and Monitoring in aggregate to address the ‘Strategic Oversight’ mode sequence. Application-level patterns often address a spectrum of discovery needs for a group of users with differing organizational responsibilities, such as management vs. detailed analysis.

6. DISCUSSION

The above analysis is based on the assumption that the user scenarios provide a unique insight into the information needs of enterprise knowledge workers. However, a number of caveats apply to both the data and the approach.

Firstly, the scenarios were originally generated to support the development of specific customer solutions rather than for the analysis above. Therefore, the principles governing their acquisition may not faithfully reflect the true distribution or priority of information needs among the various end user populations.  Secondly, the particular sample selected for this study was based on a number of pragmatic factors (including availability), which may also not faithfully represent the true distribution or priority among enterprise organizations. Thirdly, the data will inevitably contain some degree of subjectivity, particularly in cases where scenarios were generated by proxy rather than with direct end-user contact. Fourthly, the data will inevitably contain some degree of inconsistency in cases where scenarios were documented by different individuals.

We should also acknowledge a number of caveats concerning the process itself. In inductive work with foundations in qualitatively centered frameworks such as Grounded Theory, it is expected that a number of iterations of the “propose-classify-refine” cycle will be required for the process to converge on a stable output. In addition, those iterations should involve a variety of critical viewpoints, with the output tested and refined using a separate, independent sample on each iteration. Likewise, the process by which scenarios are classified would benefit from further rigour: this is a critical part of the process and relies on human judgement and inference. However, that judgement needs to go beyond simple word matching and be consistently applied to each scenario so that subtle distinctions in meaning and intent can be accurately identified and recorded.

That said, some interesting comparisons can already be made with the existing frameworks. For example, the first and third of the search modes suggested by O’Day and Jeffries have also been observed in our own study, and the second (arguably) aligns with one or more of the mode sequences identified above. Likewise, the Evaluate and Comprehend Information Behavior types identified by Cool & Belkin also appear as distinct search modes in our own taxonomy.

7. CONCLUSIONS AND FUTURE DIRECTIONS

To design better search and discovery experiences we must understand the complexities of the human-information seeking process. In this paper, we have examined the needs and behaviours of varied individuals across a range of search and discovery scenarios within various types of enterprise. In so doing, we have extended the classic IR concept of information-seeking to a broader notion of discovery-oriented problem solving, accommodating the much wider range of behaviours required to fulfil the typical goals and objectives of enterprise knowledge workers.

In addition, we have proposed a model which has at its core a taxonomy of “discovery modes” that knowledge workers employ to satisfy their information search and discovery goals. We have also examined some of the initial implications of this model for the design of more effective search and discovery platforms and tools.

Suggestions for future work include further iterations on the “propose-classify-refine” cycle using independent data. This data should ideally be acquired using a principled sampling strategy that attempts where possible to address any biases introduced in the creation of the original scenarios. In addition, this process should be complemented by empirical research and observation of knowledge workers in context to validate and refine the discovery modes and triggers that give rise to the observed patterns of usage.

8. REFERENCES

[1]          Bates, Marcia J. 1979. “Information Search Tactics.” Journal of the American Society for Information Science 30: 205-214

[2]          Bates, Marcia J. 1989. “The Design of Browsing and Berrypicking Techniques for the Online Search Interface.” Online Review 13: 407-424.

[3]          Broder, A. 2002. A taxonomy of web search, ACM SIGIR Forum, v.36 n.2, Fall 2002

[4]          Cool, C. & Belkin, N. 2002. A classification of interactions with information. In H. Bruce (Ed.), Emerging Frameworks and Methods: CoLIS4: proceedings of the Fourth International Conference on Conceptions of Library and Information Science, Seattle, WA, USA, July 21-25, 2002, (pp. 1-15).

[5]          Glaser, B. & Strauss, A. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine de Gruyter.

[6]          Jarvelin, K. and Ingwersen, P. 2004. “Information seeking research needs extension towards tasks and technology”, Information Research, Vol. 10, No. 1. (October 2004)

[7]          Kuhlthau, C. C. 1991. Inside the information search process: Information seeking from the user’s perspective. Journal of the American Society for Information Science, 42, 361-371.

[8]          Lamantia, J. 2006. “10 Information Retrieval Patterns” JoeLamantia.com, http://www.joelamantia.com/information-architecture/10-information-retrieval-patterns

[9]          Marchionini, G. 2006. Exploratory search: from finding to understanding. Commun. ACM 49(4): 41-46

[10]        Norman, Donald A. 1988. The psychology of everyday things. New York, NY, US: Basic Books.

[11]        Norman, Donald A. 2006. Logic versus usage: the case for activity centered design. Interactions 13, 6

[12]        O’Day, V. and Jeffries, R. 1993. Orienteering in an information landscape: how information seekers get from here to there. INTERCHI 1993: 438-445

[13]        Rose, D. and Levinson, D. 2004. Understanding user goals in web search, Proceedings of the 13th international conference on World Wide Web, New York, NY, USA

[14]        Salton, G. 1989. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, MA.

[15]        Spencer, D. 2006. “Four Modes of Seeking Information and How to Design for Them”. Boxes & Arrows: http://www.boxesandarrows.com/view/four_modes_of_seeking_information_and_how_to_design_for_them

[16]        Sutcliffe, A.G. and Ennis, M. 1998. Towards a cognitive theory of information retrieval. Interacting with Computers, 10:321–351.

Related Posts:

1. A Taxonomy of Enterprise Search
2. Findability is just So Last Year
3. Designing the Search Experience (tutorial at Search Solutions 2011)
4. A Taxonomy of Search Strategies and their Design Implications
5. Interaction Models for Faceted Search