Category: User Research


Defining and Applying a Language for Discovery

May 7th, 2014 — 1:10pm

Last year, I had the plea­sure of col­lab­o­rat­ing on a paper with Tony Russell-Rose and Stephann Makri that builds on and extends our work to under­stand and artic­u­late a frame­work for dis­cov­ery needs and activ­i­ties — what we refer to as the Lan­guage of Dis­cov­ery — show­ing exam­ples of con­crete appli­ca­tion and use.

It’s been a while in com­ing, but I’m happy to say the com­plete paper ‘Defin­ing and Apply­ing a Lan­guage for Dis­cov­ery’ — is avail­able now.

I’ve repro­duced the com­plete text of the paper below, and there’s also a pdf for download.

 

Abstract

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.

1 Introduction

Clas­sic IR (infor­ma­tion retrieval) is pred­i­cated on the notion of users search­ing for infor­ma­tion in order to sat­isfy a par­tic­u­lar ‘infor­ma­tion need’. How­ever, much of what we rec­og­nize as search behav­iour is often not infor­ma­tional per se. For exam­ple, Broder [2] 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 [12] have iden­ti­fied the con­sump­tion of online resources as a fur­ther com­mon cat­e­gory of search behaviour.

In this paper, we exam­ine the behav­iour of indi­vid­u­als across a range of search sce­nar­ios. These are based on an analy­sis of user needs derived from a series of cus­tomer engage­ments involv­ing the devel­op­ment of cus­tomised search applications.

The model con­sists of 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 reflects Marchionini’s frame­work [8], con­sist­ing of three ‘lookup’ modes (locate, ver­ifymon­i­tor), three ‘learn’ modes (com­pare, com­pre­hendeval­u­ate) and three ‘inves­ti­gate’ modes (explore, ana­lyze, syn­the­size).

The paper is struc­tured as fol­lows. In Sec­tion 2 we dis­cuss the modes in detail and their rela­tion­ship to exist­ing mod­els of infor­ma­tion seek­ing behav­iour. Sec­tion 3 describes the data acqui­si­tion and the analy­sis process by which the modes were derived. In Sec­tion 4 we inves­ti­gate the degree to which the model scales to accom­mo­date diverse search con­texts (e.g. from consumer-oriented web­sites to enter­prise appli­ca­tions) and dis­cuss some of the ways in which user needs vary by domain. In addi­tion, we explore the ways in which modes com­bine to form dis­tinct chains or pat­terns, and reflect on the value this offers as a frame­work for express­ing com­plex pat­terns of infor­ma­tion seek­ing behaviour.

In Sec­tion 5 we exam­ine the prac­ti­cal impli­ca­tions of the model, dis­cussing how it can be applied in the design of inter­ac­tive appli­ca­tions, at both the level of indi­vid­ual modes and as com­pos­ite struc­tures. Finally, in Sec­tion 6 we reflect on the gen­eral util­ity of such mod­els and frame­works, and explore briefly the qual­i­ties that might facil­i­tate their increased adop­tion by the wider user expe­ri­ence design community.

2 Models of Information Seeking

The frame­work pro­posed in this study is influ­enced by a num­ber of pre­vi­ous mod­els. For exam­ple, Bates [1] iden­ti­fies a set of 29 search ‘tac­tics’ which she organ­ised into four broad cat­e­gories, includ­ing mon­i­tor­ing (“to keep a search on track”). Like­wise, O’Day & Jef­fries [11] exam­ined the use of infor­ma­tion search results by clients of pro­fes­sional infor­ma­tion inter­me­di­aries and iden­ti­fied three cat­e­gories of behav­iour, includ­ing mon­i­tor­ing a known topic or set of vari­ables over time and explor­ing a topic in an undi­rected fash­ion. They also observed that a given search sce­nario would often evolve into a series of inter­con­nected searches, delim­ited by trig­gers and stop con­di­tions that sig­nalled tran­si­tions between modes within an over­all scenario.

Cool & Belkin [3] pro­posed a clas­si­fi­ca­tion of inter­ac­tion with infor­ma­tion which included eval­u­ate and com­pre­hend. They also pro­posed cre­ate and mod­ify, which together reflect aspects of our syn­the­size mode.

Ellis and his col­leagues [4, 5, 6] devel­oped a model con­sist­ing of a num­ber of broad infor­ma­tion seek­ing behav­iours, includ­ing mon­i­tor­ing and ver­i­fy­ing(“check­ing the infor­ma­tion and sources found for accu­racy and errors”). In addi­tion, his brows­ing mode (“semi-directed search­ing in an area of poten­tial inter­est”) aligns with our def­i­n­i­tion of explore. He also noted that it is pos­si­ble to dis­play more than one behav­iour at any given time. In revis­it­ing Ellis’s find­ings among social sci­en­tists, Meho and Tibbo [10] iden­ti­fied analysing (although they did not elab­o­rate on it in detail). More recently, Makri et al [8] pro­posed search­ing(“for­mu­lat­ing a query in order to locate infor­ma­tion”), which reflects to our own def­i­n­i­tion of locate.

In addi­tion to the research-oriented mod­els out­lined above, we should also con­sider practitioner-oriented frame­works. Spencer [14] sug­gests four modes of infor­ma­tion seek­ing, includ­ing known-item (a sub­set of our locate mode) andexploratory (which mir­rors our def­i­n­i­tion of explore). Laman­tia [7] also iden­ti­fies four modes, includ­ing mon­i­tor­ing.

In this paper, we use the char­ac­ter­is­tics of the mod­els above as a lens to inter­pret the behav­iours expressed in a new source of empir­i­cal data. We also exam­ine the com­bi­na­to­r­ial nature of the modes, extend­ing Ellis’s [5] con­cept of mode co-occurrence to iden­tify and define com­mon pat­terns and sequences of infor­ma­tion seek­ing behaviour.

3 Studying Search Behaviour

3.1 Data Acquisition

The pri­mary source of data in this study is a set of 381 infor­ma­tion needs cap­tured dur­ing client engage­ments involv­ing the devel­op­ment of a num­ber of cus­tom 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:

  • Find best offers before the oth­ers do so I can have a high margin.
  • Get help and guid­ance on how to sell my car safely so that I can achieve a good price.
  • Under­stand what is sell­ing by area/region so I can source the cor­rect stock.
  • Under­stand a portfolio’s expo­sures to assess invest­ment mix
  • Under­stand the per­for­mance of a part in the field so that I can deter­mine if I should replace 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. [5], [10]), 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. [8]), 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.

3.2 Data Analysis

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 [14]. Inevitably, this process was some­what sub­jec­tive, echo­ing the obser­va­tions made by Bates [1] 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.”

In this respect, the process was par­tially deduc­tive, in apply­ing the insights from exist­ing mod­els to clas­sify the data in a top-down man­ner. But it was also par­tially induc­tive, apply­ing a bottom-up, grounded analy­sis to iden­tify new types of behav­iour not present in the orig­i­nal mod­els or to sug­gest revised def­i­n­i­tions of exist­ing behaviours.

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.

The process resulted in the iden­ti­fi­ca­tion of nine pri­mary search modes, which are defined below along with an exam­ple sce­nario (from the domain of consumer-oriented search):

1. LocateTo 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. It is con­sis­tent with (but a super­set of) Spencer’s [14] known item search mode. This was the most fre­quent mode in the site search sce­nar­ios (120 instances, which con­trasts with just 2 for enter­prise search).

2. Ver­ifyTo con­firm that an item meets some spe­cific, objec­tive cri­te­rion, e.g. “See the cor­rect price for sin­gles and deals”. Often found in com­bi­na­tion with locat­ing, this mode is con­cerned with val­i­dat­ing the accu­racy of some data item, com­pa­ra­ble to that pro­posed by Ellis et al.  [5] (39 site search instances, 4 for enter­prise search).

3. Mon­i­torMain­tain aware­ness of the sta­tus of an item for pur­poses of man­age­ment or con­trol, e.g. “Alert me to new resources in my area”. This activ­ity focuses on the state of asyn­chro­nous respon­sive­ness and is con­sis­tent with that of Bates [1], O’Day and Jef­fries [11], Ellis [4], and Laman­tia [7] (13 site search instances, 17 for enter­prise search).

4. Com­pareTo iden­tify sim­i­lar­i­ties & dif­fer­ences within a set of items, e.g. “Com­pare cars that are my pos­si­ble can­di­dates in detail”. This mode has not fea­tured promi­nently in most of the pre­vi­ous mod­els (with the pos­si­ble excep­tion of Marchionini’s), but accounted for a sig­nif­i­cant pro­por­tion of enter­prise search behav­iour [13]. Although a com­mon fea­ture on many ecom­merce sites, it occurred rel­a­tively infre­quently in the site search data (2 site search instances, 16 for enter­prise search).

5. Com­pre­hendTo gen­er­ate inde­pen­dent insight by inter­pret­ing pat­terns within a data set, e.g. “Under­stand what my com­peti­tors are sell­ing”. This activ­ity focuses on the cre­ation of knowl­edge or under­stand­ing and is con­sis­tent with that of Cool & Belkin [3] and Mar­chion­ini [9] (50 site search instances, 12 for enter­prise search).

6. Eval­u­ateTo use judge­ment to deter­mine the value of an item with respect to a spe­cific goal, e.g. “I want to know whether my agency is deliv­er­ing best value”. This mode is sim­i­lar in spirit to ver­ify, in that it is con­cerned with val­i­da­tion of the data. How­ever, while ver­ify focuses on sim­ple, objec­tive fact check­ing, our con­cep­tion of eval­u­ate involves more sub­jec­tive, knowledge-based judge­ment, sim­i­lar to that pro­posed by Cool & Belkin [3] (61 site search instances, 78 for enter­prise search).

7. ExploreTo inves­ti­gate an item or data set for the pur­pose of knowl­edge dis­cov­ery, e.g. “Find use­ful stuff on my sub­ject topic”. In some ways the bound­aries of this mode are 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 [11] explor­ing a topic in an undi­rected fash­ion and Spencer’s [14] exploratory (110 site search instances, 16 for enter­prise search).

8. Ana­lyzeTo exam­ine an item or data set to iden­tify pat­terns & rela­tion­ships,e.g. Ana­lyze the mar­ket so I know where my strengths and weak­nesses are”. This mode fea­tures less promi­nently in pre­vi­ous mod­els, appear­ing as a sub-component of the pro­cess­ing stage in Meho & Tibbo’s [10] model, and over­lap­ping some­what with Cool & Belkin’s [3] orga­nize. This def­i­n­i­tion is also con­sis­tent with that of Makri et al. [8], who iden­ti­fied analysing as an impor­tant aspect of lawyers’ inter­ac­tive infor­ma­tion behav­iour and defined it as “exam­in­ing in detail the ele­ments or struc­ture of the con­tent found dur­ing information-seeking.” (p. 630). This was the most com­mon ele­ment of the enter­prise search sce­nar­ios (58 site search instances, 84 for enter­prise search).

9. Syn­the­sizeTo cre­ate a novel or com­pos­ite arte­fact from diverse inputs, e.g. “I need to cre­ate a read­ing list on celebrity spon­sor­ship”. This mode also appears as a sub-component of the pro­cess­ing stage in Meho & Tibbo’s [10] model, and involves ele­ments of Cool & Belkin’s [3] cre­ate and use. Of all the modes, this one is the most com­monly asso­ci­ated with infor­ma­tion use in its broad­est sense (as opposed to infor­ma­tion seek­ing). It was rel­a­tively rare within site search (5 site search instances, 15 for enter­prise search).

Although the modes were gen­er­ated from an inde­pen­dent data source and analy­sis process, we have ret­ro­spec­tively explored the degree to which they align with exist­ing frame­works, e.g. Marchionini’s [8]. In this con­text, locate, ver­ify, andmon­i­tor could be described as lower-level ‘lookup’ modes, com­pare, com­pre­hend, and eval­u­ate as ‘learn’ modes and explore, ana­lyze, and syn­the­size as higher-level ‘inves­ti­gate’ modes.

4 Mode Sequences and Patterns

The modes defined above pro­vide an insight into the needs of users of site search and enter­prise search appli­ca­tions and a frame­work for under­stand­ing human infor­ma­tion seek­ing behav­iour. But their real value lies not so much in their occur­rence as indi­vid­ual instances but in the pat­terns of co-occurrence they reveal. In most sce­nar­ios, modes com­bine to form dis­tinct chains and pat­terns, echo­ing the tran­si­tions observed by O’Day and Jef­fries [11] and the com­bi­na­to­r­ial behav­iour alluded to by Ellis [5], who sug­gested that infor­ma­tion behav­iours can often be nested or dis­played in parallel.

Typ­i­cally these pat­terns con­sist of chains of length two or three, often with one par­tic­u­lar mode play­ing a dom­i­nant role. Site search, for exam­ple, was char­ac­ter­ized by the fol­low­ing patterns:

  1. Insight-driven search: (Explore-Analyze– Com­pre­hend): This pat­terns rep­re­sents an exploratory search for insight or knowl­edge to resolve an explicit infor­ma­tion need,  e.g. “Assess the proper mar­ket value for my car
  2. Oppor­tunis­tic search: (Explore-Locate-Evaluate): In con­trast to the explicit focus of Insight-driven search, this sequence rep­re­sents a less directed explo­ration in the prospect of serendip­i­tous dis­cov­ery e.g. “Find use­ful stuff on my sub­ject topic
  3. Qual­i­fied search (Locate-Verify) This pat­tern 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

By con­trast, enter­prise search was char­ac­ter­ized by a larger num­ber of more diverse sequences, such as:

  1. Com­par­a­tive search: (Analyze-Compare– Eval­u­ate) e.g. “Replace a prob­lem­atic part with an equiv­a­lent or bet­ter part with­out com­pro­mis­ing qual­ity and cost
  2. Exploratory search: (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

A fur­ther insight into these pat­terns can be obtained by pre­sent­ing them in dia­gram­matic form. Fig­ure 1 illus­trates sequences 1–3 above plus other com­monly found site search pat­terns as a net­work (with sequence num­bers shown on the arrows). It shows how cer­tain modes tend to func­tion as “ter­mi­nal” nodes, i.e. entry points or exit points for a given 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 shad­ing indi­cates the mode ‘level’ alluded to ear­lier: light tones indi­cate ‘lookup’ modes, mid tones are the ‘learn’ modes, and dark tones are the ‘inves­ti­gate’ modes.

Fig. 1. Mode network for site searchFig. 1. Mode net­work for site search 

Fig­ure 2 illus­trates sequences 4–8 above plus other com­monly found pat­terns in the enter­prise search data.

Fig. 2. Mode network for enterprise searchFig. 2. Mode net­work for enter­prise search 

The pat­terns described above allow us to reflect on some of the dif­fer­ences between the needs of site search users and those of enter­prise search. Site search, for exam­ple, is char­ac­ter­ized by an empha­sis on sim­pler “lookup” behav­iours such as Locate and Ver­ify (120 and 39 instances respec­tively); modes which were rel­a­tively rare in enter­prise search (2 and 4 instances respec­tively). By con­trast, enter­prise search is char­ac­ter­ized by higher-level “learn” and “inves­ti­gate” behav­iours such as Ana­lyze and Eval­u­ate (84 and 78 instances respec­tively, com­pared to 58 and 61 for site search). Inter­est­ingly, in nei­ther case was the stereo­type of ‘search equals find­abil­ity’ borne out: even in site search (whereLocate was the most com­mon mode), known-item search was account­able for no more than a quar­ter of all instances.

But per­haps the biggest dif­fer­ence is in the com­po­si­tion of the chains: enter­prise search is char­ac­terised by a wide vari­ety of het­ero­ge­neous chains, while site searched focuses on a small num­ber of com­mon tri­grams and bigrams. More­over, the enter­prise search chains often dis­played a frac­tal nature, in which cer­tain chains were embed­ded within or trig­gered by oth­ers, to cre­ate larger, more com­plex sequences of behaviour.

5 Design Implications

Although the model offers a use­ful frame­work for under­stand­ing human infor­ma­tion seek­ing behav­iour, its real value lies in its use as a prac­ti­cal design resource. As such, it can pro­vide guid­ance on issues such as:

  • the fea­tures and func­tion­al­ity that should be avail­able at spe­cific points within a system;
  • the inter­ac­tion design of indi­vid­ual func­tions or components;
  • the design cues used to guide users toward spe­cific areas of task interface.

More­over, the model also has sig­nif­i­cant impli­ca­tions for the broader aspects of user expe­ri­ence design, such as the align­ment between the over­all struc­ture or con­cept model of a sys­tem and its users’ men­tal mod­els, and the task work­flows for var­i­ous users and con­texts. This broader per­spec­tive addresses archi­tec­tural ques­tions such as the nature of the work­spaces required by a given appli­ca­tion, or the paths that users will take when nav­i­gat­ing within a system’s struc­ture.  In this way, the modes also act as a gen­er­a­tive tool for larger, com­pos­ite design issues and structures.

5.1 Indi­vid­ual modes

On their own, each of the modes describes a type of behav­iour that may need to be sup­ported by a given infor­ma­tion system’s design. For exam­ple, an online retail site should sup­port locat­ing and com­par­ing spe­cific prod­ucts, and ide­ally alsocom­pre­hend­ing dif­fer­ences and eval­u­at­ing trade­offs between them. Like­wise, an enter­prise appli­ca­tion for elec­tronic com­po­nent selec­tion should sup­port mon­i­tor­ingand ver­i­fy­ing the suit­abil­ity of par­tic­u­lar parts, and ide­ally also ana­lyz­ing andcom­pre­hend­ing any rel­e­vant pat­terns and trends in their life­cy­cle. By under­stand­ing the antic­i­pated search modes for a given sys­tem, we can opti­mize the design to sup­port spe­cific user behav­iours. In the fol­low­ing sec­tion we con­sider indi­vid­ual instances of search modes and explore some of their design implications.

Locate

This mode encap­su­lates the stereo­typ­i­cal ‘find­abil­ity’ task that is so com­monly asso­ci­ated with site search. But sup­port for this mode can go far beyond sim­ple key­word entry. For exam­ple, by allow­ing the user to choose from a list of can­di­dates, auto-complete trans­forms the query for­mu­la­tion prob­lem from one of recall into one of recog­ni­tion (Fig­ure 3).

Figure 1: Auto-complete supports LocatingFig. 3. Auto-complete sup­ports locating 

Like­wise, Amazon’s par­tial match strat­egy deals with poten­tially failed queries by iden­ti­fy­ing the key­word per­mu­ta­tions that are likely to pro­duce use­ful results. More­over, by ren­der­ing the non-matching key­words in strikethrough text, it facil­i­tates a more informed approach to query refor­mu­la­tion (Fig­ure 4).

Figure 2: Partial matches support LocatingFig 4: Par­tial matches sup­port Locating 

Ver­ify

In this mode, the user is inspect­ing a par­tic­u­lar item and wish­ing to con­firm that it meets some spe­cific cri­te­rion.  Google’s image results page pro­vides a good exam­ple of this (see Fig­ure 5).

Figure 3: Search result previews support verificationFig 5: Search result pre­views sup­port verification 

On mouseover, the image is zoomed in to show a mag­ni­fied ver­sion along with key meta­data, such as file­name, image size, cap­tion, and source. This allows the user to ver­ify the suit­abil­ity of a spe­cific result in the con­text of its alter­na­tives. Like­wise, there may be cases where the user needs to ver­ify a par­tic­u­lar query rather than a par­tic­u­lar result. In pro­vid­ing real-time feed­back after every key press, Google Instant sup­ports ver­i­fi­ca­tion by pre­view­ing the results that will be returned for a given query (Fig­ure 6). If the results seem unex­pected, the user can check the query for errors or try alter­na­tive spellings or key­word combinations.

Figure 4: Instant results supports verification of queriesFig 6: Instant results sup­ports ver­i­fi­ca­tion of queries 

Com­pare

The Com­pare mode is fun­da­men­tal to online retail, where users need to iden­tify the best option from the choices avail­able. A com­mon tech­nique is to pro­vide a cus­tom view in which details of each item are shown in sep­a­rate columns, enabling rapid com­par­i­son of prod­uct attrib­utes. Best Buy, for exam­ple, sup­ports com­par­i­son by organ­is­ing the attrib­utes into log­i­cal groups and auto­mat­i­cally high­light­ing the dif­fer­ences (Fig­ure 7).

Figure 5: Separate views support product comparisonFig 7: Sep­a­rate views sup­port prod­uct comparison 

But com­par­i­son is not restricted to qual­i­ta­tive attrib­utes. In finan­cial ser­vices, for exam­ple, it is vital to com­pare stock per­for­mance and other finan­cial instru­ments with indus­try bench­marks. Google Finance sup­ports the com­par­i­son of secu­ri­ties through a com­mon chart­ing com­po­nent (Fig­ure 8).

Figure 6: Common charts allow comparison of quantitative dataFig 8: Com­mon charts allow com­par­i­son of quan­ti­ta­tive data 

Explore

A key prin­ci­ple in explor­ing is dif­fer­en­ti­at­ing between where you are going andwhere you have already been. In fact, this dis­tinc­tion is so impor­tant that it has been woven into the fab­ric of the web itself; with unex­plored hyper­links ren­dered in blue by default, and vis­ited hyper­links shown in magenta. Ama­zon takes this prin­ci­ple a step fur­ther, through com­po­nents such as a  ‘Recent Searches’ panel show­ing the pre­vi­ous queries issued in the cur­rent ses­sion, and a ‘Recent His­tory’ panel show­ing the items recently viewed (Fig­ure 9).

Figure 7: Recent history supports explorationFig 9: Recent his­tory sup­ports exploration 

Another sim­ple tech­nique for encour­ag­ing explo­ration is through the use of “see also” pan­els. Online retail­ers com­monly use these to pro­mote related prod­ucts such as acces­sories and other items to com­ple­ment an intended pur­chase. An exam­ple of this can be seen at Food Net­work, in which fea­tured videos and prod­ucts are shown along­side the pri­mary search results (Fig­ure 10).

Figure 8: ‘See Also’ panels support explorationFig 10: ‘See Also’ pan­els sup­port exploration 

A fur­ther tech­nique for sup­port­ing explo­ration is through the use of auto-suggest. While auto-complete helps users get an idea out of their heads and into the search box, auto-suggest throws new ideas into the mix. In this respect, it helps users explore by for­mu­lat­ing more use­ful queries than they might oth­er­wise have thought of on their own. Home Depot, for exam­ple, pro­vides a par­tic­u­larly exten­sive auto-suggest func­tion con­sist­ing of prod­uct cat­e­gories, buy­ing guides, project guides and more, encour­ag­ing the dis­cov­ery of new prod­uct ideas and con­tent (Fig­ure 11).

Figure 9: Auto-suggest supports exploratory search Fig 11: Auto-suggest sup­ports exploratory search 

Ana­lyze

In modes such as explor­ing, the user’s pri­mary con­cern is in under­stand­ing theover­all infor­ma­tion space and iden­ti­fy­ing areas to ana­lyze in fur­ther detail. Analy­sis, in this sense, goes hand in hand with explor­ing, as together they present com­ple­men­tary modes that allow search to progress beyond the tra­di­tional con­fines of infor­ma­tion retrieval or ‘findability’.

A sim­ple exam­ple of this could be found at Google patents (Fig­ure 12). The alter­nate views (Cover View and List View) allow the user to switch between rapid explo­ration (scan­ning titles, brows­ing thumb­nails, look­ing for infor­ma­tion scent) and a more detailed analy­sis of each record and its metadata.

Figure 10: Alternate views support mode switching between exploration and analysisFig 12: Alter­nate views sup­port mode switch­ing between explo­ration and analysis 

In the above exam­ple the analy­sis focuses on qual­i­ta­tive infor­ma­tion derived from pre­dom­i­nantly tex­tual sources. Other appli­ca­tions focus on quan­ti­ta­tive data in the form of aggre­gate pat­terns across col­lec­tions of records. News­Sift, for exam­ple, pro­vided a set of data visu­al­iza­tions which allowed the user to ana­lyze results for a given news topic at the aggre­gate level, gain­ing an insight that could not be obtained from exam­in­ing indi­vid­ual records alone (Fig­ure 13).

Figure 11: Visualizations support analysis of quantitative informationFig 13: Visu­al­iza­tions sup­port analy­sis of quan­ti­ta­tive information 

5.2 Com­pos­ite patterns

The exam­ples above rep­re­sent instances of indi­vid­ual modes, show­ing var­i­ous ways they can be sup­ported by one or more aspects of a system’s design. How­ever, a key fea­ture of the model is its empha­sis on the com­bi­na­to­r­ial nature of modes and the pat­terns of co-occurrence this reveals [12]. In this respect, its true value is in help­ing design­ers to address more holis­tic, larger scale con­cerns such as the appro­pri­ate struc­ture, con­cept model, and orga­niz­ing prin­ci­ples of a sys­tem, as well as the func­tional and infor­ma­tional con­tent of its major com­po­nents and con­nec­tions between them.

Design at this level relies on trans­lat­ing com­pos­ite modes and chains that rep­re­sent sense-making activ­i­ties – often artic­u­lated as user jour­neys through a task and infor­ma­tion space – into inter­ac­tion com­po­nents that rep­re­sent mean­ing­ful com­bi­na­tions of infor­ma­tion and dis­cov­ery capa­bil­i­ties [13].  These com­po­nents serve as ‘build­ing blocks’ that design­ers can assem­ble into larger com­pos­ite struc­tures to cre­ate a user expe­ri­ence that sup­ports the antic­i­pated user jour­neys and aligns with their users’ men­tal mod­els [14].

The pop­u­lar micro-blogging ser­vice twitter.com pro­vides a num­ber of exam­ples of the cor­re­spon­dence between com­pos­ite modes and inter­ac­tion com­po­nents assem­bled at var­i­ous lev­els to pro­vide a coher­ent user expe­ri­ence architecture.

Header Bar

The header bar at the top of most pages of twitter.com com­bines sev­eral infor­ma­tional and func­tional ele­ments together in a sin­gle com­po­nent that sup­ports a num­ber of modes and mode chains (Fig­ure 14). It includes four dynamic sta­tus indi­ca­tors that address key aspects of twitter’s con­cept model and the users’ men­tal models:

  • the pres­ence of new tweets by peo­ple the user follows
  • inter­ac­tions with other twit­ter users such as fol­low­ing them or men­tion­ing them in a tweet
  • activ­ity related to the user’s pro­file, such as their lat­est tweets and shared media
  • peo­ple, top­ics, or items of inter­est sug­gested by the sys­tems rec­om­mender functions

These sta­tus indi­ca­tor icons update auto­mat­i­cally and pro­vide links to spe­cific pages in the twitter.com appli­ca­tion archi­tec­ture that pro­vide fur­ther detail on each area of focus. The header bar thus enables Mon­i­tor­ing of a user’s activ­ity within the full scope of the twitter.com net­work; i.e. its con­tent, mem­bers, their activ­i­ties, etc.  The header bar also enables Mon­i­tor­ing activ­ity within almost all the work­spaces that users encounter in the course of their pri­mary jour­neys throughtwitter.com.

Fig. 14. twitter.com Header BarFig. 14. twitter.com Header Bar 

The Strate­gic Over­sight chain (Mon­i­tor – Ana­lyze – Eval­u­ate) is a fun­da­men­tal sequence for twit­ter users, repeated fre­quently with dif­fer­ent aspects of the user’s pro­file. The header bar sup­ports the first step of this chain, in which users Mon­i­tor the net­work for con­tent and activ­ity of inter­est to them, and then tran­si­tion to Analy­sis and Eval­u­a­tion of that activ­ity by nav­i­gat­ing to des­ti­na­tion pages for fur­ther detail.

The header bar also includes a search box fea­tur­ing auto-complete and auto-suggest func­tion­al­ity, which pro­vides sup­port for the Qual­i­fied Search mode chain (Locate – Ver­ify). The search box also enables users to ini­ti­ate many other mode chains by sup­port­ing the Explore mode. These include Exploratory Search (Explore – Ana­lyze – Eval­u­ate), Insight-driven Search (Explore – Ana­lyze – Com­pre­hend), and Opportunity-driven Search (Explore – Locate – Eval­u­ate). All these mode chains over­lap by shar­ing a com­mon start­ing point. This is one of the most read­ily rec­og­niz­able kinds of com­po­si­tion, and often cor­re­sponds to a sin­gle instance of a par­tic­u­lar inter­ac­tion component.

The header bar includes sup­port for post­ing or Syn­the­siz­ing new tweets, reflect­ing the fact that the cre­ation of new con­tent is prob­a­bly the sec­ond most impor­tant indi­vid­ual mode (after Mon­i­tor­ing). A menu of links to admin­is­tra­tive pages and func­tions for man­ag­ing one’s twit­ter account com­pletes the con­tent of the header bar.

Indi­vid­ual Tweets

The indi­vid­ual tweets and activ­ity updates that make up the stream at the heart of the pri­mary work­space are the most impor­tant inter­ac­tion com­po­nents of the twit­ter expe­ri­ence, and their design shows a direct cor­re­spon­dence to many com­pos­ite modes and chains (Fig­ure 15). Indi­vid­ual items pro­vide the con­tent of a tweet along with the author’s pub­lic name, their twit­ter user­name, pro­file image, and the time elapsed since the tweet’s cre­ation. Together, these details allow users to Com­pare and Com­pre­hend the con­tent and sig­nif­i­cance of tweets in their own stream.  As users read more tweets and begin to rec­og­nize authors and top­ics, they can Com­pare, Ana­lyze, and Eval­u­ate them.  The indi­ca­tors of ori­gin and activ­ity allow users to Com­pare and Com­pre­hend the top­ics and inter­ests of other twit­ter users.

Fig. 15. Individual TweetFig. 15. Indi­vid­ual Tweet 

Options to invoke a num­ber of func­tions that cor­re­spond to other dis­cov­ery modes are embed­ded within the indi­vid­ual items in the stream. For exam­ple, if an update was retweeted, it is marked as such with the orig­i­nal author indi­cated and their pro­file page linked. It also shows the num­ber of times the tweet has been retweeted and favor­ited, with links that open modal pre­views of the list of users who did so. This sup­ports Mon­i­tor­ing, Explo­ration and Com­pre­hen­sion of the sig­nif­i­cance and atten­tion an indi­vid­ual tweet has received, while the links sup­port Loca­tion, Ver­i­fi­ca­tion and Mon­i­tor­ing of the other users who retweeted or favor­ited it.

Pub­lic pro­file names and user­names are linked to pages which sum­ma­rize the activ­i­ties and rela­tion­ships of the author of a tweet, enabling users to Locate and Ver­ify authors, then tran­si­tion to Mon­i­tor­ing, Explor­ing and Com­pre­hend­ing their activ­i­ties, inter­ests, and how they are con­nected to the rest of the twit­ter network.

Hash­tags are pre­sented with dis­tinct visual treat­ment.  When users click on one, it ini­ti­ates a search using the hash­tag, allow­ing users to Locate, Explore, Com­pre­hend, and Ana­lyze the topic referred to, any con­ver­sa­tions in which the tag is men­tioned, and the users who employ the tag.

Fig. 16. Expanded TweetFig. 16. Expanded Tweet 

Longer tweets are trun­cated, offer­ing an ‘Expand’ link which opens a panel dis­play­ing the num­ber of retweets and favourites and the images of the users who did so, along with the date and time of author­ing and a link to a ‘details’ page for a per­ma­nent URL that other users and exter­nal ser­vices can ref­er­ence (Fig­ure 16). This sort of trun­ca­tion enables users to more eas­ily Explore the full set of tweets in a stream and Locate indi­vid­ual items of inter­est. Con­versely, the ‘Expand’ panel allows the user to more eas­ily Explore and Com­pre­hend indi­vid­ual items.

Tweets that con­tain links to other tweets offer a ‘View tweet’ link, which opens a panel dis­play­ing the full con­tents of the orig­i­nal tweet, the date and time of post­ing, the num­ber of retweets and favorites and a pre­view list of the users who did so.  The ‘View tweet’ link thus sup­ports the Locate, Explore, and Com­pre­hend modes for indi­vid­ual updates.

Tweets that con­tain links to dig­i­tal assets such as pho­tos, videos, songs, pre­sen­ta­tions, and doc­u­ments, offer users the abil­ity to pre­view these assets directly within an expanded dis­play panel, pro­vid­ing sup­port for the Locate, Explore, and Com­pre­hend modes. These pre­views link to the source of the assets, enabling users to Locate them.  Users can also ‘flag’ media for review by twit­ter (e.g. due to vio­la­tion of poli­cies about sen­si­tive or ille­gal imagery) – which is a very spe­cific form of Evaluation.

Fig. 17. Tweet Displaying a PhotoFig. 17. Tweet Dis­play­ing a Photo 

Tweets that con­tain links to items such as arti­cles pub­lished by news­pa­pers, mag­a­zines, and jour­nals, or rec­og­nized des­ti­na­tions such as Foursquare and Google + pages, offer a ‘Sum­mary’ link (Fig­ure 17). This link opens a panel that presents the first para­graph of the arti­cle or des­ti­na­tion URL, an image from the orig­i­nal pub­lisher, and a list of users who have retweeted or favor­ited it, thus sup­port­ing Loca­tion, Explo­ration and Ver­i­fi­ca­tion of the linked item.

A text input field seeded with the author’s user­name allows users to reply to spe­cific tweets directly from an indi­vid­ual update. Users can also ‘retweet’ items directly from the list. Both func­tions are forms of Syn­the­sis, and encour­age users to cre­ate fur­ther con­tent and rela­tion­ships within the network.

Users can mark tweets as ‘favorites’ to indi­cate the impor­tance or value of these tweets to oth­ers; a clear exam­ple of the Eval­u­a­tion mode. Favorites also allow users to build a col­lec­tion of tweets curated for retrieval and inter­pre­ta­tion, enabling the Locate, Com­pare, Com­pre­hend, and Ana­lyze modes for tweets as indi­vid­ual items or as groups.

A ‘More’ link opens a menu offer­ing ‘Email Tweet’ and ‘Embed Tweet’ options, allow­ing users to ini­ti­ate tasks that take tweets out­side the twit­ter envi­ron­ment.  These two func­tions sup­port infor­ma­tion usage modes, rather than search anddis­cov­ery modes, so their dis­tinct treat­ment – invoked via a dif­fer­ent inter­ac­tion than the other func­tions – is con­sis­tent with the great empha­sis the twit­ter expe­ri­ence places on dis­cov­ery and sense mak­ing activities.

If the tweet is part of a con­ver­sa­tion, a ‘View this con­ver­sa­tion’ link allows read­ers to open a panel that presents related tweets and user activ­ity as a sin­gle thread, accom­pa­nied by a reply field.  This pro­vides sup­port for the Locate, Explore, Com­pre­hend, Ana­lyze, Eval­u­ate and Syn­the­size modes (Fig­ure 18).

Fig. 18. Tweet Showing a Conversation Fig. 18. Tweet Show­ing a Conversation 

The infor­ma­tional and func­tional con­tent pre­sented by indi­vid­ual items in their var­i­ous forms enables a num­ber of mode chains. These include Strate­gic Over­sight, in which users main­tain aware­ness of con­ver­sa­tions, top­ics, other users, and activ­i­ties; Strate­gic Insight, wherein users focus on and derive insight into con­ver­sa­tions, top­ics, and other users; and Com­par­a­tive Syn­the­sis, in which users real­ize new insights and cre­ate new con­tent through direct engage­ment with con­ver­sa­tions, top­ics, and other users.

In a man­ner sim­i­lar to the search box, this inter­ac­tion com­po­nent serves as an ini­ti­a­tion point for a num­ber of mode chains, includ­ing Exploratory Search, Insight-driven Search, and Opportunity-driven Search. Indi­vid­ual tweets thus com­bine sup­port for many impor­tant modes and mode chains into a sin­gle inter­ac­tion com­po­nent.  As a con­se­quence, they need to be rel­a­tively rich and ‘dense’, com­pact­ing much func­tion­al­ity into a sin­gle inter­ac­tion com­po­nent, but this reflects their cru­cial role in the user jour­neys that char­ac­ter­ize the twit­ter experience.

Pri­mary Work­spaces and Pages

In the pre­vi­ous sec­tion we reviewed the cor­re­spon­dence between groups of modes and the inter­ac­tion com­po­nents of a user expe­ri­ence. In this sec­tion, we review the ways in which modes and chains impact the com­po­si­tion and pre­sen­ta­tion of the next level of UX struc­ture within the sys­tem: work spaces.

The pri­mary work­spaces of twitter.com all empha­size inter­ac­tion with a stream of indi­vid­ual updates, but the focus and con­tent vary depend­ing on the con­text. On the Home page, for exam­ple, the cen­tral stream con­sists of tweets from peo­ple the user fol­lows, while on the ‘Me’ page the stream con­sists of the tweets cre­ated by the user (Fig­ure 19). How­ever, the lay­out of these pages remains con­sis­tent: the work­space is dom­i­nated by a sin­gle cen­tral stream of indi­vid­ual updates. The pri­mary inter­ac­tion mode for this stream is Mon­i­tor­ing, evi­dent from the count of new items added to the net­work since the last page refresh.

Fig. 19. twitter.com Home WorkspaceFig. 19. twitter.com Home Workspace 

The place­ment of the header bar at the top of all of the pri­mary work­spaces is a design deci­sion that reflects the pri­macy of Mon­i­tor­ing as a mode of engage­ment with the twit­ter ser­vice; sup­port­ing its role as a per­sis­tent ‘back­ground’ mode of dis­cov­ery inde­pen­dent of the user’s cur­rent point in a task or jour­ney, and its role as a com­mon entry point to the other mode chains and user journeys.

The con­sis­tent place­ment of the ‘Com­pose new Tweet’ con­trol in upper right cor­ner of the work­space reflects known inter­ac­tion design prin­ci­ples (cor­ners are the sec­ond most eas­ily engaged areas of a screen, after the cen­tre) and the under­stand­ing that Syn­the­sis is the sec­ond most impor­tant sin­gle mode for the twit­ter service.

The con­tent of the indi­vid­ual updates attracts and retains users’ atten­tion very effec­tively: the major­ity of the actions a user may want to take in regard to a tweet (or any of the related con­structs in twitter’s con­cept model such as con­ver­sa­tions, hash tags, pro­files, linked media, etc.) are directly avail­able from the inter­ac­tion com­po­nent.  In some cases, these actions are pre­sented via modal or light­box pre­view, wherein the user’s focus is ‘forced’ onto a sin­gle ele­ment – thus main­tain­ing the pri­macy of the stream.  In oth­ers, links lead to des­ti­na­tion pages that switch the user’s focus to a dif­fer­ent sub­ject – another user’s pro­file, for exam­ple – but in most of these cases the struc­ture of the work­space remains con­sis­tent: a two col­umn body sur­mounted by the ubiq­ui­tous header bar. There is lit­tle need to look else­where in the work­space, unless the user needs to check the sta­tus of one of the broader aspects of their account, at which point the header bar pro­vides appro­pri­ate func­tion­al­ity as dis­cussed above.

The absence of a page footer – scrolling is ‘infi­nite’ on the pri­mary pages oftwitter.com – reflects the con­scious deci­sion to con­vey updates as an end­less, dynamic stream.  This encour­ages users to con­tinue scrolling, increas­ing Explo­ration activ­ity, and enhanc­ing users’ Com­pre­hen­sion of addi­tional updates – which ben­e­fits twitter’s busi­ness by increas­ing the atten­tion users direct toward the service.

Although the two-tier, stream-centred struc­ture of twitter’s pri­mary work­spaces remains con­sis­tent, there are vari­a­tions in the com­po­si­tion of the left col­umn (Fig­ure 20). On the Home page, for exam­ple, the left col­umn offers four sep­a­rate com­po­nents. The first is a sum­mary of the user’s pro­file, includ­ing a pro­file image, a link to their pro­file page, counts of their tweets, fol­low­ers, and the peo­ple they fol­low, and a ‘com­pose new tweet’ box.  This is another exam­ple of a com­po­nent sup­port­ing a com­pos­ite of modes.

Fig. 20. Twitter Home Page - Left ColumnFig. 20. Twit­ter Home Page – Left Column 

The core pur­pose is to enable users to Mon­i­tor the most impor­tant aspects of their own account via the counts.  The links pro­vide direct Locate func­tion­al­ity for fol­low­ers, tweets, and accounts the user fol­lows; and also serve as a point of depar­ture for the same mode chains that can be ini­ti­ated from the header bar.  The ‘com­pose new tweet’ func­tion encour­ages users to cre­ate updates, under­lin­ing the impor­tance of Syn­the­sis as the source of new con­tent within the twit­ter network.

User Expe­ri­ence Architecture

The twitter.com expe­ri­ence is intended to sup­port a set of user jour­neys con­sist­ing largely of search and dis­cov­ery tasks which cor­re­spond with spe­cific mon­i­tor­ing and search-related mode chains. Fur­ther, we can see that pat­terns of recur­rence, inter­sec­tion, over­lap, and sequenc­ing in the aggre­gate set of search and dis­cov­ery modes are sub­stan­tially reflected in twitter’s user expe­ri­ence architecture.

From a struc­tural design per­spec­tive, the core [16] of the twitter.com user expe­ri­ence archi­tec­ture is a set of four inter­ac­tion con­soles, each of which focuses on mon­i­tor­ing a dis­tinct stream of updates around the most impor­tant facets of thetwitter.com con­cept model: the con­tent and activ­i­ties of peo­ple in the user’s per­sonal net­work (Home); inter­ac­tions with other users (Inter­ac­tions); the user’s pro­file (@Me); and a digest of con­tent from all users in the twitter.com net­work (Dis­cover) (Fig­ure 21).

The core mon­i­tor­ing con­soles are sup­ported by screens that assist and encour­age users to expand their per­sonal net­works through loca­tion and explo­ration tools; these include ‘Find friends’, ‘Who to fol­low’ ‘Browse cat­e­gories’, and the search results page.

Fig. 21. Twitter.com Discover WorkspaceFig. 21. Twitter.com Dis­cover Workspace 

Spe­cific land­ing pages pro­vide mon­i­tor­ing and cura­tion tools for the dif­fer­ent types of rela­tion­ships users can estab­lish in the social graph: fol­low and un-follow, fol­low­ers and fol­low­ing, pub­lic and pri­vate accounts, list mem­ber­ships, etc.  A small set of screens pro­vides func­tion­al­ity for admin­is­ter­ing the user’s account, such as ‘Settings’.

Under­ly­ing this user expe­ri­ence archi­tec­ture is a con­cept model con­sist­ing pri­mar­ily of a small set of social objects – tweets, con­ver­sa­tions, pro­files, shared dig­i­tal assets, and lists thereof – linked together by search and dis­cov­ery verbs. A rel­a­tively sim­ple infor­ma­tion archi­tec­ture estab­lishes the set of cat­e­gories used to iden­tify these objects by topic, sim­i­lar­ity, and con­tent (Fig­ure 22).

In its holis­tic and gran­u­lar aspects, the twit­ter user expe­ri­ence archi­tec­ture aligns well with users’ men­tal mod­els for build­ing a pro­file and par­tic­i­pat­ing in an ongo­ing stream of con­ver­sa­tions. How­ever, what emerges quite quickly from analy­sis of the twit­ter con­cept model and user expe­ri­ence archi­tec­ture is the role of search and dis­cov­ery modes in both atomic and com­pos­ite forms at every level of twitter’s design. Rather than merely sub­sum­ing modes as part of some larger activ­ity, many of the most com­mon actions users can take with twitter’s core inter­ac­tion objects cor­re­spond directly to modes themselves.

Fig. 22. Twitter.com User Experience ArchitectureFig. 22. Twitter.com User Expe­ri­ence Architecture 

The indi­vid­ual tweet com­po­nent is a prime exam­ple: the sum­maries of author pro­files and their recent activ­ity are a com­pos­ite of the Locate, Explore and Com­pre­hend modes (Fig­ure 23). Evi­dently, the pre­sen­ta­tion, labelling, and inter­ac­tion design may reflect adap­ta­tions spe­cific to the lan­guage and men­tal model of the twit­ter envi­ron­ment, but the activ­i­ties are clearly rec­og­niz­able. The ‘Show con­ver­sa­tion’ func­tion dis­cussed above also reflects direct sup­port to Locate, Explore and Com­pre­hend a con­ver­sa­tion object as a sin­gle interaction.

Fig. 23. Twitter Profile SummaryFig. 23. Twit­ter Pro­file Summary 

Because the twitter.com expe­ri­ence is so strongly cen­tred on sense-making, search and dis­cov­ery modes often directly con­sti­tute the activ­ity paths con­nect­ing one object to another within the user expe­ri­ence archi­tec­ture.  In this sense, the modes and chains could be said to act as a ‘skele­ton’ for twitter.com, and are directly vis­i­ble to an unprece­dented degree in the inter­ac­tion design built on that skeleton.

6 Discussion

The model described in this paper encom­passes a range of infor­ma­tion seek­ing behav­iours, from ele­men­tary lookup tasks through to more com­plex problem-solving activ­i­ties. How­ever, the model could also be framed as part of a broader set of infor­ma­tion behav­iours, extend­ing from ‘acqui­si­tion’ ori­ented tasks at one end of the spec­trum to ‘usage’ ori­ented activ­i­ties at the other (Fig­ure 24). In this con­text, modes can span more than one phase. For exam­ple, Explore entails a degree ofinter­ac­tion cou­pled with the antic­i­pa­tion of fur­ther dis­cov­ery, i.e. acqui­si­tion.  Like­wise, Eval­u­ate implies a degree of inter­ac­tion in the pur­suit of some higher goal or pur­pose to which the out­put will be put, i.e. usage.

It would appear that with the pos­si­ble excep­tion of syn­the­size, there are no exclu­sively usage-oriented behav­iours in the model. This may sug­gest that the model is in some senses incom­plete, or may sim­ply reflect the con­text in which the data was acquired and the IR-centric processes by which it was analysed.

Reduc­ing the ‘scope’ of the model such that modes serve only as descrip­tors of dis­tilled sense-making activ­ity inde­pen­dent of con­text (such as the user’s over­all goal and the nature of the infor­ma­tion assets involved) may help clar­ify the rela­tion­ship between acqui­si­tion, inter­ac­tion and usage phases. In this per­spec­tive, there appears to be a form of ‘par­al­lelism’ in effect; with users simul­ta­ne­ously under­tak­ing activ­i­ties focused on an over­all goal, such as Eval­u­at­ing the qual­ity of a finan­cial instru­ment, while also per­form­ing activ­i­ties focused on nar­rower information-centred objec­tives such as Locat­ing and Ver­i­fy­ing the util­ity of the infor­ma­tion assets nec­es­sary for them to com­plete the Eval­u­a­tion.  These ‘par­al­lel’ sets of activ­i­ties – one focused on infor­ma­tion assets in ser­vice to a larger goal, and the other focused on the goal itself – can be use­fully described in terms of modes, and what is more impor­tant, seem inter­twined in the minds of users as they artic­u­late their dis­cov­ery needs.

Fig. 24. From information acquisition to information useFig. 24. From infor­ma­tion acqui­si­tion to infor­ma­tion use 

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. Evi­dently, such an approach is not unique: Makri (2008), 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 a nat­ural bound­ary on the scope and extent of the pat­terns they rep­re­sent. Their for­mat also allows a researcher 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 behav­iours 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 behav­iour but through a com­bi­na­tion of other nested behav­iours, or may sim­ply sat­isfy the need in a way that had not been envis­aged at all.

Evi­dently, the process of infer­ring infor­ma­tion behav­iour from self-reported needs can never be wholly deter­min­is­tic, regard­less of the con­sis­tency mea­sures dis­cussed in Sec­tion 3.1. In this respect, fur­ther steps should be taken to oper­a­tional­ize the process and develop some inde­pen­dent mea­sure of sta­bil­ity or objec­tiv­ity in its usage, so that its value and insights can extend reli­ably to the wider research community.

The com­po­si­tional behav­iour of the modes sug­gests fur­ther open ques­tions and avenues for research. One of these is the nature of com­po­si­tion­al­ity itself: one the one hand it could be thought of as a pseudo-linguistic gram­mar, with bigrams and tri­grams of modes that com­bine in turn to form larger sequences, anal­o­gous to coher­ent “sen­tences”. In this con­text, the modes act as verbs, while the asso­ci­ated objects (users, infor­ma­tion assets, processes etc.) become the nouns. The occur­rence of dis­tinct ‘open­ing’ and ‘clos­ing’ modes in the sce­nar­ios would seem to fur­ther sup­port this view. How­ever, in some sce­nar­ios the tran­si­tions between the modes are far less appar­ent, and instead they could be seen as apply­ing in par­al­lel, like notes com­bin­ing in har­mony to form a musi­cal chord. In both cases, the degree and nature of any such com­po­si­tional rules needs fur­ther empir­i­cal inves­ti­ga­tion. This may reveal other depen­den­cies yet to be observed, such as the pos­si­bil­ity alluded to ear­lier of higher-level behav­iours requir­ing the com­ple­tion of cer­tain lower level modes before they them­selves can terminate.

The process of map­ping from modes to design inter­ven­tions also reveals fur­ther obser­va­tions on the util­ity of infor­ma­tion mod­els in gen­eral. Despite their evi­dent value as ana­lyt­i­cal frame­works and their pop­u­lar­ity among researchers (Bates’ Berryp­ick­ing model has been cited over 1,000 times, for exam­ple), few have gained sig­nif­i­cant trac­tion within the design com­mu­nity, and fewer still are adopted as part of the main­stream work­ing prac­tices of sys­tem design practitioners.

In part, this may be sim­ply a reflec­tion of imper­fect chan­nels of com­mu­ni­ca­tion between the research and design com­mu­ni­ties. How­ever, it may also reflect a grow­ing con­cep­tual gap between research insights on the one hand and cor­re­spond­ing design inter­ven­tions on the other. It is likely that the most valu­able the­o­ret­i­cal mod­els will need to strike a bal­ance between flex­i­bil­ity (the abil­ity to address a vari­ety of domains and prob­lems), gen­er­a­tive power (the abil­ity to express com­plex pat­terns of behav­iour) and an appro­pri­ate level of abstrac­tion (such that design insights are read­ily avail­able; or may be inferred with min­i­mal speculation).

7 Conclusions

In this paper, we have exam­ined the needs and behav­iours of indi­vid­u­als across a wide range of search and dis­cov­ery sce­nar­ios. We have pro­posed a model of infor­ma­tion seek­ing behav­iour which has at its core a set of modes that peo­ple reg­u­larly employ to sat­isfy their infor­ma­tion needs. In so doing, we explored a novel, goal-driven approach to elic­it­ing user needs, and iden­ti­fied some key dif­fer­ences in user behav­iour between site search and enter­prise search.

In addi­tion, we have demon­strated the value of the model as a frame­work for express­ing com­plex pat­terns of search behav­iour, extend­ing the IR con­cept of information-seeking to embrace a broader range of infor­ma­tion inter­ac­tion and use behav­iours. We pro­pose that our approach can be adopted by other researchers who want to adopt a ‘needs first’ per­spec­tive to under­stand­ing infor­ma­tion behaviour.

By illus­trat­ing ways in which indi­vid­ual modes are sup­ported in exist­ing search appli­ca­tions, we have made a prac­ti­cal con­tri­bu­tion that helps bridge the gap between inves­ti­gat­ing search behav­iour and design­ing appli­ca­tions to sup­port such behav­iour. In par­tic­u­lar, we have demon­strated how modes can serve as an effec­tive design tool across var­ied lev­els of sys­tem design: con­cept model, UX archi­tec­ture, inter­ac­tion design, and visual design.

References

  1. Bates, Mar­cia J. 1979. Infor­ma­tion Search Tac­tics. Jour­nal of the Amer­i­can Soci­ety for Infor­ma­tion Sci­ence 30, 205–214.
  2. Cool, C. & Belkin, N. 2002. A clas­si­fi­ca­tion of inter­ac­tions with infor­ma­tion. In H. Bruce (Ed.), Emerg­ing Frame­works and Meth­ods: CoLIS4: pro­ceed­ings of the 4th Inter­na­tional Con­fer­ence on Con­cep­tions of Library and Infor­ma­tion Sci­ence, Seat­tle, WA, USA, July 21–25, 1–15.
  3. Ellis, D.  1989.  A Behav­ioural Approach to Infor­ma­tion Retrieval Sys­tem Design.  Jour­nal of Doc­u­men­ta­tion, 45(3), 171–212.
  4. Ellis, D., Cox, D. & Hall, K. 1993.  A Com­par­i­son of the Information-seeking Pat­terns of Researchers in the Phys­i­cal and Social Sci­ences.  Jour­nal of Doc­u­men­ta­tion 49(4), 356–369.
  5. Ellis, D. & Hau­gan, M. 1997.  Mod­el­ling the Information-seeking Pat­terns of Engi­neers and Research Sci­en­tists in an Indus­trial Envi­ron­ment.  Jour­nal of Doc­u­men­ta­tion 53(4), pp. 384–403.
  6. Hobbs, J. (2005) An intro­duc­tion to user jour­neys. Boxes & Arrows. [Avail­able: http://www.boxesandarrows.com/an-introduction-to-user-journeys/
  7. Kalbach, J. (2012). Design­ing Screens Using Cores and Paths. Boxes & Arrows. [Avail­able:http://www.boxesandarrows.com/designing-screens-using-cores-and-paths/
  8. Laman­tia, J. 2006. 10 Infor­ma­tion Retrieval PatternsJoeLamantia.com. [Avail­able:http://www.joelamantia.com/information-architecture/10-information-retrieval-patterns.
  9. Laman­tia, J. (2009). Cre­at­ing Suc­cess­ful Por­tals with a Design Frame­work. Inter­na­tional Jour­nal of Web Por­tals (IJWP), 1(4), 63–75. doi:10.4018/jwp.2009071305
  10. Makri, S., Bland­ford, A. & Cox, A.L. 2008.  Inves­ti­gat­ing the Information-Seeking Behav­iour of Aca­d­e­mic Lawyers:  From Ellis’s Model to Design. Infor­ma­tion Pro­cess­ing and Man­age­ment 44(2), 613–634.
  11. Mar­chion­ini, G. 2006. Exploratory search: from find­ing to under­stand­ing. Com­mu­ni­ca­tions of the ACM 49(4), 41–46.
  12. Meho, L. & Tibbo, H. 2003.  Mod­el­ing the Information-seeking Behav­ior of Social Sci­en­tists: Ellis’s Study Revis­ited.  Jour­nal of the Amer­i­can Soci­ety for Infor­ma­tion Sci­ence and Tech­nol­ogy 54(6), 570–587.
  13. O’Day, V. & Jef­fries, R. 1993. Ori­en­teer­ing in an Infor­ma­tion Land­scape: How Infor­ma­tion Seek­ers get from Here to There.INTERCHI 1993, 438–445.
  14. Rose, D. and Levin­son, D. 2004. Under­stand­ing user goals in web search, Pro­ceed­ings of the 13th inter­na­tional con­fer­ence on World Wide Web, New York, NYUSA
  15. Russell-Rose, T., Laman­tia, J. and Bur­rell, M. 2011. A Tax­on­omy of Enter­prise Search and Dis­cov­ery. Pro­ceed­ings of HCIR 2011,Cal­i­for­nia, USA.
  16. Russell-Rose, T. and Makri, S. (2012). A Model of Con­sumer Search Behav­ior. Pro­ceed­ings of Euro­HCIR 2012, Nijmegen, Nether­lands.
  17. Spencer, D. 2006. Four Modes of Seek­ing Infor­ma­tion and How to Design for Them. Boxes & Arrows. [Avail­able:www.boxesandarrows.com/view/four_modes_of_seeking_information_and_how_to_design_for_them

 

Comment » | Information Architecture, Language of Discovery, User Research

Data Science Highlights: An Investigation of the Discipline

March 28th, 2014 — 1:26pm

I’ve posted a sub­stan­tial read­out sum­ma­riz­ing some of the more salient find­ings from a long-running pro­gram­matic research pro­gram into data sci­ence. This deck shares syn­the­sized find­ings around many of the facets of data sci­ence as a dis­ci­pline, includ­ing prac­tices, work­flow, tools, org mod­els, skills, etc. This read­out dis­tills a very wide range of inputs, includ­ing; direct inter­views, field-based ethnog­ra­phy, com­mu­nity par­tic­i­pa­tion (real-world and on-line), sec­ondary research from indus­try and aca­d­e­mic sources, analy­sis of hir­ing and invest­ment activ­ity in data sci­ence over sev­eral years, descrip­tive and def­i­n­i­tional arti­facts authored by prac­ti­tion­ers / ana­lysts / edu­ca­tors, and other exter­nal actors, media cov­er­age of data sci­ence, his­tor­i­cal antecedents, the struc­ture and evo­lu­tion of pro­fes­sional dis­ci­plines, and even more.

I con­sider it a sort of business-anthropology-style inves­ti­ga­tion of data sci­ence, con­ducted from the view­point of prod­uct making’s pri­mary aspects; strat­egy, man­age­ment, design, and delivery.

I learned a great deal dur­ing the course of this effort, and expect to con­tinue to learn, as data sci­ence will con­tinue to evolve rapidly for the next sev­eral years.

Data sci­ence prac­ti­tion­ers look­ing at this mate­r­ial are invited to pro­vide feed­back about where these mate­ri­als are accu­rate or inac­cu­rate, and most espe­cially about what is miss­ing, and what is com­ing next for this very excit­ing field.

 

 

Data Sci­ence High­lights from Joe Laman­tia

 

 

Comment » | Big Data, User Research

Understanding Data Science: Two Recent Studies

October 22nd, 2013 — 7:40am

If you need such a deeper under­stand­ing of data sci­ence than Drew Conway’s pop­u­lar venn dia­gram model, or Josh Wills’ tongue in cheek char­ac­ter­i­za­tion, “Data Sci­en­tist (n.): Per­son who is bet­ter at sta­tis­tics than any soft­ware engi­neer and bet­ter at soft­ware engi­neer­ing than any sta­tis­ti­cian.” two rel­a­tively recent stud­ies are worth reading.

Ana­lyz­ing the Ana­lyz­ers,’ an O’Reilly e-book by Har­lan Har­ris, Sean Patrick Mur­phy, and Marck Vais­man, sug­gests four dis­tinct types of data sci­en­tists — effec­tively per­sonas, in a design sense — based on analy­sis of self-identified skills among prac­ti­tion­ers.  The sce­nario for­mat dra­ma­tizes the dif­fer­ent per­sonas, mak­ing what could be a dry sta­tis­ti­cal read­out of sur­vey data more engag­ing.  The survey-only nature of the data,  the restric­tion of scope to just skills, and the sug­gested mod­els of skill-profiles makes this feel like the sort of exer­cise that data sci­en­tists under­take as an every day task; col­lect­ing data, ana­lyz­ing it using a mix of sta­tis­ti­cal tech­niques, and shar­ing the model that emerges from the data min­ing exer­cise.  That’s not an indict­ment, sim­ply an obser­va­tion about the con­sis­tent feel of the effort as a prod­uct of data sci­en­tists, about data science.

And the paper ‘Enter­prise Data Analy­sis and Visu­al­iza­tion: An Inter­view Study’ by researchers Sean Kan­del, Andreas Paepcke, Joseph Heller­stein, and Jef­fery Heer con­sid­ers data sci­ence within the larger con­text of indus­trial data analy­sis, exam­in­ing ana­lyt­i­cal work­flows, skills, and the chal­lenges com­mon to enter­prise analy­sis efforts, and iden­ti­fy­ing three arche­types of data sci­en­tist.  As an interview-based study, the data the researchers col­lected is richer, and there’s cor­re­spond­ingly greater depth in the syn­the­sis.  The scope of the study included a broader set of roles than data sci­en­tist (enter­prise ana­lysts) and involved ques­tions of work­flow and orga­ni­za­tional con­text for ana­lyt­i­cal efforts in gen­eral.  I’d sug­gest this is use­ful as a primer on ana­lyt­i­cal work and work­ers in enter­prise set­tings for those who need a base­line under­stand­ing; it also offers some gen­uinely inter­est­ing nuggets for those already famil­iar with dis­cov­ery work.

We’ve under­taken a con­sid­er­able amount of research into dis­cov­ery, ana­lyt­i­cal work/ers, and data sci­ence over the past three years — part of our pro­gram­matic approach to lay­ing a foun­da­tion for prod­uct strat­egy and high­light­ing inno­va­tion oppor­tu­ni­ties — and both stud­ies com­ple­ment and con­firm much of the direct research into data sci­ence that we con­ducted. There were a few impor­tant dif­fer­ences in our find­ings, which I’ll share and dis­cuss in upcom­ing posts.

Comment » | Language of Discovery, User Research

Big Data Is Not the Insight: Slides From Enterprise Search Europe

May 21st, 2013 — 12:42pm

Slides from my talk Big Data Is Not the Insight: The Lan­guage of Dis­cov­ery at Enter­prise Search Europe in Lon­don last week are avail­able for view­ing and down­load from slideshare. The con­fer­ence was a good gath­er­ing of lead­ing per­spec­tives on search in Europe, def­i­nitely one I’d look for­ward to attend­ing again. And of course Lon­don is lovely in May, even when it feels more like win­ter than spring…

Big Data Is Not the Insight: The Lan­guage Of Dis­cov­ery: from Joe Laman­tia

Comment » | Language of Discovery, User Experience (UX), User Research

UX Australia Recording Available

February 19th, 2013 — 11:38am

The audio record­ing of my pre­sen­ta­tion Design­ing Big Data Inter­ac­tions in the Age of Insight from UX Aus­tralia 2012 was just published.

It’s avail­able for direct down­load from the ses­sion page, in the iTunes store, and as part of the pod­cast series for all the ses­sions at UX Australia.

Comment » | Language of Discovery, User Experience (UX), User Research

Enterprise Search Europe

February 19th, 2013 — 11:20am

I’ll be pre­sent­ing recent work around the evolv­ing Lan­guage of Dis­cov­ery at the Enter­prise Search Europe con­fer­ence in Lon­don this May. Tyler Tate — co-author of Design­ing the Search Expe­ri­ence — and I are shar­ing a ses­sion on ‘cre­at­ing effec­tive interfaces’.

In addi­tion to the reg­u­lar ses­sions, Tony Russell-Rose is pre­sent­ing a work­shop titled Search Inter­face Opti­mi­sa­tion (to use the British spelling) on Tues­day that promises to inform and enhance your under­stand­ing of how peo­ple search, and your toolkit for design­ing good search experiences.

ESS is the pre­mier gath­er­ing of indus­try prac­ti­tion­ers active in the search and dis­cov­ery spaces, and the ros­ter of speak­ers looks strong; if you need to engage with or learn some­thing from the com­mu­nity, this is the place to do so.

And Lon­don is won­der­ful in the spring — hope to see some of you there!

Comment » | Language of Discovery, User Experience (UX), User Research

A Taxonomy of Enterprise Search: Complete Paper

September 18th, 2011 — 10:50am

The Euro­HCIR orga­niz­ers have pub­lished pro­ceed­ings from this year’s work­shop (which I was unfor­tu­nately unable to attend), which means I can make our paper A Tax­on­omy of Enter­prise Search and Dis­cov­ery directly avail­able.  The com­plete pro­ceed­ings are here, and are also pack­aged as a sin­gle down­load.

Here’s the text of the pub­lished paper, includ­ing ref­er­ences, and adding in a few illus­tra­tions omit­ted to meet page lim­its on papers.  Many thanks go to co-authors Tony Russell-Rose and Mark Bur­rell for putting this paper together.

A Tax­on­omy of Enter­prise Search

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. For exam­ple, 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 known item) or trans­ac­tional (e.g. to find a sites through which the user can trans­act, e.g. through online shop­ping, social media, etc.). Sim­i­larly, Rose & Levin­son (2004) have iden­ti­fied con­sump­tion of online resources as a fur­ther cat­e­gory of search behav­iour and query intent.

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.

Cat­e­gories and Sub­ject Descriptors

H.3.3 [Infor­ma­tion Search and Retrieval]: Search process;

H.3.5 [Online Infor­ma­tion Ser­vices]: Web-based services

Gen­eral Terms

Human Fac­tors.

Key­words

Enter­prise search, infor­ma­tion seek­ing, user behav­iour, knowl­edge work­ers, search modes, infor­ma­tion dis­cov­ery, user expe­ri­ence design.

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 1998) and the dynamic model (Bates, 1989). In addi­tion, oth­ers have inves­ti­gated search as a strate­gic process, exam­in­ing the var­i­ous prob­lem solv­ing strate­gies and tac­tics that infor­ma­tion seek­ers employ over extended peri­ods of time (e.g. Kuhlthau, 1991).

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 the sce­nar­ios derived from numer­ous engage­ments involv­ing the devel­op­ment of search and busi­ness intel­li­gence solu­tions uti­liz­ing 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, which “orga­nizes accord­ing to usage” whereas “…tra­di­tional human cen­tred design orga­nizes accord­ing to topic, in iso­la­tion, out­side the con­text of real, every­day use.” (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 an alter­na­tive model focused on infor­ma­tion dis­cov­ery rather than infor­ma­tion seek­ing per se, which has at its core an ini­tial tax­on­omy of the “modes of dis­cov­ery” 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 both 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. In recog­ni­tion of this, alter­na­tive mod­els of infor­ma­tion seek­ing have been pro­posed. 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 con­nec­tions between the dynamic model and 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 “search modes” or major 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. More­over, O’Day & Jef­fries also attempted to char­ac­terise the analy­sis tech­niques employed by the clients in inter­pret­ing the search results, iden­ti­fy­ing the fol­low­ing six pri­mary cat­e­gories: (1) Look­ing for trends or cor­re­la­tions; (2) Mak­ing com­par­isons; (3) Exper­i­ment­ing with dif­fer­ent aggregations/scaling; (4) Iden­ti­fy­ing crit­i­cal sub­sets; (5) Mak­ing assess­ments; (6) Inter­pret­ing data to find meaning.

More recent inves­ti­ga­tions into the rela­tion­ship between infor­ma­tion needs and search activ­i­ties include that of Mar­chion­ini (2005), who iden­ti­fies three major cat­e­gories of search activ­ity, namely “Lookup”, “Learn” and “Investigate”.

3. A TAXONOMY OF ENTERPRISE SEACH AND DISCOVERY

The pri­mary source of data in this study is a set of user sce­nar­ios cap­tured dur­ing numer­ous engage­ments involv­ing the devel­op­ment of search and busi­ness intel­li­gence solu­tions uti­liz­ing the Endeca Lat­i­tude soft­ware plat­form. These sce­nar­ios 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. For exam­ple, in each of the sce­nar­ios above there is an artic­u­la­tion of the need to develop an under­stand­ing or com­pre­hen­sion of some aspect of the data, imply­ing that “com­pre­hend­ing” may con­sti­tute one such dis­cov­ery mode. Inevitably, this analy­sis process was some­what iter­a­tive and sub­jec­tive, echo­ing the obser­va­tions made by Bates (1979) in the iden­ti­fi­ca­tion of her 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); and Com­pre­hen­sive­ness (they address the full range of dis­cov­ery scenarios).

The ini­tial set of dis­cov­ery modes to emerge from this analy­sis con­sists of a set of nine, arranged into three top-level cat­e­gories con­sis­tent with those of Mar­chion­ini (2005). The nine modes are as fol­lows, each shown 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, the out­put of this process has been opti­mized for the cur­rent data set and in that respect rep­re­sents an ini­tial inter­pre­ta­tion that will need to evolve fur­ther. For exam­ple, “mon­i­tor­ing” may appear to be a lookup activ­ity when con­sid­ered in the con­text of a sim­ple alert mes­sage, but when viewed as a strate­gic activ­ity per­formed by an exec­u­tive in the con­text of an organ­i­sa­tional dash­board, a much greater degree of inter­ac­tion and com­plex­ity is implied. Con­versely, “explor­ing” is a con­cept whose level of abstrac­tion may prove some­what higher than the oth­ers, thus break­ing 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 flex­i­bly accom­mo­dat­ing arbi­trary vari­a­tions in domain, infor­ma­tion resources, etc.

4. MODE SEQUENCES AND PATTERNS

A fur­ther inter­est­ing obser­va­tion aris­ing from the above analy­sis is that the map­ping between sce­nar­ios and modes is not one-to–one. Instead, some sce­nar­ios are seen to involve a num­ber of modes, some­times with a pri­mary or dom­i­nant mode, and often with an implied lin­ear sequence. More­over, cer­tain sequences of modes tend to re-occur more fre­quently than oth­ers, form­ing spe­cific “mode chains” or pat­terns, anal­o­gous to higher-level syn­tac­tic units. These pat­terns pro­vide a frame­work for under­stand­ing the tran­si­tions between modes (echo­ing the trig­gers iden­ti­fied by O’Day & Jef­fries), and allude to the exis­tence of nat­ural seams that can be used be used to pro­vide fur­ther insight into infor­ma­tion 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 four [ele­men­tal] modes” (Laman­tia 2006).

Mode Net­works

Fig­ure 1. Dis­cov­ery mode network

The five most fre­quent mode pat­terns are listed below. These have been assigned descrip­tive (if some­what infor­mal) labels to aid their char­ac­ter­i­sa­tion, along with the sequence of modes they rep­re­sent 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 the form of 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 and scale 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 — should allow the modes to serve as a lan­guage for the design of vari­able scale activity-centered dis­cov­ery solu­tions through 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.

Fig­ure: Dash­board 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.

Fig­ure: Visual Analy­sis Screen

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.

Fig­ure: Dif­fer­en­ti­ated Appli­ca­tion Structure

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6. DISCUSSION

The above analy­sis is pred­i­cated on the notion 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 a spe­cific imple­men­ta­tion rather than for the analy­sis above. There­fore, the prin­ci­ples gov­ern­ing their cre­ation may not faith­fully reflect the true dis­tri­b­u­tion or pri­or­ity of infor­ma­tion needs among the var­i­ous end user pop­u­la­tions. Sec­ondly, the par­tic­u­lar sam­ple we selected for this study was based on a num­ber of prag­matic fac­tors (includ­ing avail­abil­ity), which may 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 a “propose-classify-refine” cycle will be required for the process to con­verge on a sta­ble out­put (e.g. Rose & Levin­son, 2004). 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 of course relies on human judge­ment and infer­ence, but 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 iden­ti­fied as dis­tinct dis­cov­ery modes in our own study, and the sec­ond (arguably) maps on to one or more of the mode chains iden­ti­fied above. Like­wise, the search results analy­sis tech­niques that O’Day & Jef­fries iden­ti­fied also present some inter­est­ing parallels.

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 an alter­na­tive model focused on infor­ma­tion dis­cov­ery rather than infor­ma­tion seek­ing which has at its core a tax­on­omy of “modes of dis­cov­ery” 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 based on 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] 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.

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

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

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

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

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

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

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

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

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

 

 

Comment » | Enterprise, Language of Discovery, User Research

Presenting "A Taxonomy of Enterprise Search" at EUROHCIR

June 6th, 2011 — 8:13am

I’m pleased to be pre­sent­ing ‘A Tax­on­omy of Enter­prise Search’ at the upcom­ing Euro­HCIR work­shop, part of the 2011 HCI con­fer­ence in the UK.  Co-authored with Tony Russell-Rose of UXLabs, and Mark Bur­rell here at Endeca, this is our first pub­li­ca­tion of some of the very excit­ing work we’re doing to under­stand and describe dis­cov­ery activ­i­ties in enter­prise set­tings, and do so within a changed and broader fram­ing than tra­di­tional infor­ma­tion retrieval.  The paper builds on work I’ve done pre­vi­ously on under­stand­ing and defin­ing infor­ma­tion needs and pat­terns of infor­ma­tion retrieval activ­ity, while work­ing on search and dis­cov­ery prob­lems as part of larger user expe­ri­ence archi­tec­ture efforts.

Here’s the abstract of the paper:

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

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.

There’s a con­sid­er­able amount of research avail­able on infor­ma­tion retrieval — even within a com­par­a­tively new dis­ci­pline like HCIR, focused on the human to sys­tem inter­ac­tion aspects of IR — but I think it’s the attempt to define an activ­ity cen­tered gram­mar for inter­act­ing with infor­ma­tion that makes our approach worth exam­in­ing.  The HCIR events in the U.S. (and now Europe) blend aca­d­e­mic and prac­ti­tioner per­spec­tives, so are an appro­pri­ate audi­ence for our pro­posed vocab­u­lary of dis­cov­ery activ­ity ‘modes’ that’s based on a sub­stan­tial body of data col­lected and ana­lyzed dur­ing solu­tion design and deploy­ment engagements.

I’ll post the paper itself once the pro­ceed­ings are available.

 

 

 

Comment » | Language of Discovery, User Experience (UX), User Research

Design For Goals: JBoye09 Workshop Slides

November 25th, 2009 — 5:42am

I’ve posted the slides from my tuto­r­ial / work­shop Design For Goals at JBoye 09 on slideshare: they’re embed­ded below.

The struc­ture for this tuto­r­ial is part method review (on how to under­stand people’s goals in a struc­tured way), and part shar­ing of re-usable pat­terns found after research­ing goals.   Since the con­text of ori­gin for both the goals and pat­terns was com­plex inter­na­tional finance, some trans­la­tion of the raw mate­ri­als and exam­ples and the syn­the­sized pat­terns into a realm closer to home for ordi­nary peo­ple is likely in order.

As you’re going through the slides, I sug­gest using your own activ­i­ties that involve infor­ma­tion find­ing and mak­ing sub­stan­tial finan­cial deci­sions as a ref­er­ence.  Not all the exam­ples that I selected as the basis of exer­cises dur­ing the tuto­r­ial made across the cul­tural bar­rier between North Amer­ica and North­ern Europe: I was sur­prised at how many peo­ple (in a pro­fes­sional audi­ence) have never bought house or car…  Which proves yet again that this is one of the areas for user expe­ri­ence design to work on as a discipline.

And as we had a small, noisy, and rather warm room right after lunch, I should say big thanks to all the par­tic­i­pants and vol­un­teers — every­one — who made an effort to engage.

Even design edu­ca­tion is a work-in-progress, it seems.

2 comments » | Customer Experiences, User Experience (UX), User Research

Does Being Ethical Pay?

May 12th, 2008 — 11:16am

Com­pa­nies spend huge amounts of money to be ‘socially respon­si­ble.’ Do con­sumers reward them for it? And how much?’ is the leader for a short piece titled Does Being Eth­i­cal Pay? just pub­lished in Sloan Man­age­ment Review. The quick answer is “Yes”, so it’s worth read­ing fur­ther to learn the spe­cific ways that eth­i­cal­ity plays into people’s spend­ing deci­sions.
Here’s an excerpt:

In all of our tests, con­sumers were will­ing to pay a slight pre­mium for the eth­i­cally made goods. But they went much fur­ther in the other direc­tion: They would buy uneth­i­cally made prod­ucts only at a steep dis­count.


What’s more, con­sumer atti­tudes played a big part in shap­ing those results. Peo­ple with high stan­dards for cor­po­rate behav­ior rewarded the eth­i­cal com­pa­nies with big­ger pre­mi­ums and pun­ished the uneth­i­cal ones with big­ger dis­counts.

At least accord­ing to this research, being eth­i­cal is a nec­es­sary attribute for a prod­uct.
There are clear impli­ca­tions for prod­uct design: ethics should be on the table as a con­cern at all stages of prod­uct devel­op­ment, from ideation and con­cept­ing of new prod­ucts, to the mar­ket­ing and sales of fin­ished prod­ucts.
And these (lim­ited, cer­tainly not the final word) find­ings match with the idea of adding ethics to the set of impor­tant user expe­ri­ence qual­i­ties cap­tured in Peter Morville’s UX Hon­ey­comb.
The (Aug­mented) Eth­i­cal UX Hon­ey­comb
ethical_small_honeycombbig.png
How are user expe­ri­ence design­ers tak­ing the eth­i­cal qual­i­ties of their work into account?

Comment » | Ethics & Design, User Research

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