Human Information Interaction Fidel, Raya

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Fidel, Raya. Human Information Interaction: An Ecological Approach to Information Behavior. The MIT Press, 2012. Project MUSE. muse.jhu.edu/book/21630. https://muse.jhu.edu/.

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5 Five Search Strategies

The concept search strategy has been part of the vocabulary of human information

behavior (HIB) since the earliest user studies. However, researchers only began to

investigate search strategies after the development of digital technology, when the

concept became a popular focus of study with the introduction of the World Wide

Web. Unlike information need , which is relatively stable, 1 search strategy addresses the

dynamic part of the search process itself. While an information need triggers a search

process, search strategies reflect the activities during the search. In addition, strategies

are considered to possess a great advantage as an object of study: While they are purely

cognitive in nature, they are observable because their use — that is, the activities during

a search — can be observed. 2 New research techniques that have been afforded by

digital technology made it possible to investigate the search process itself, and thus

its strategies.

Because the concept search strategy is relatively concrete and observable, its defini-

tion has not raised much discussion, but researchers have attributed to it a range of

interpretations and definitions and have often overlooked the need to provide their

construal even when search strategies were the focus of their studies. This chapter

briefly provides a few examples of some of these definitions and proposes a view on

search strategies that is relevant to the design of information systems.

5.1 What Is a Search Strategy?

Research into search strategies has been carried out since the late 1970s, but the inter-

pretation of the concept search strategies has been highly fluid, and even today the

concept is imbued with a plurality of meanings. HIB researchers have applied the term

to signify any aspect of an information search process that lacks its own name. Most

empirical researchers have also neglected to explain their understanding of the concept.

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In some cases, the investigators ’ construal can be inferred from the specific search

strategy they investigated. Only a few researchers provided explicit definitions for

search strategies , and some others borrowed these definitions for their own studies.

5.1.1 Implicit Construal of Search Strategy

Examples of search strategies that have been discovered in web searching without the

support of an explicit definition of the concept show that most address specific actions

in a search process and are highly concrete, mechanical, and concerned with observ-

able actions. Only a few implicit definitions enjoy some level of abstraction. Some

researchers were inconsistent in the level of abstraction of the search strategies they

investigated, identifying them along a range from highly concrete to the abstract.

The series of studies that Nigel Ford and his colleagues conducted is a typical

example of a concrete and actions-based interpretation of the concept. Ford began his

investigation of search strategies during the early period of bibliographical databases

(e.g., Ford, Wood, and Walsh 1994). Examining his research reports, it seems that he

understood search strategies to be the types of actions a searcher take to transform a

query. A recent article about the use of search strategies provided 18 strategies (Ford,

Eaglestone, and Madden 2009), such as page down , remove Boolean operators , include

quotation marks , reuse part of a query , and change operators only . Other researchers — such

as Martzoukou (2008) and Iivonen and White (2001) — recognized search strategies on

the same level of abstraction, identifying, for example, use Boolean operators or use

subject directory .

The concrete level of the search strategies ’ construal limits the range of their appli-

cability because they are to a large degree determined by the technology being used.

Search strategies that can be employed in best-match systems, 3 for instance, are dif-

ferent from those in systems with ranked output. 4 Moreover, some of the search strate-

gies that were identified are based on specific technical attributes of the search system,

such as the query language (e.g., include quotation marks , use subject directory ) and query

operators (e.g., use Boolean operators ). As a result, the strategies that were discovered

are pertinent to searches under the conditions in which they were discovered, but

they may not be applicable to other modes of information searching, such as browsing

the library shelves or asking a person for driving directions. The more abstract the

level of definition, the more modes of searching it represents.

A few scholars interpreted search strategies on a somewhat abstract level. An

example of such approach is the study by Ramirez et al. (2002) which examined the

role of computers in mediating human-to-human communication, that is, informa-

tion-seeking when the source of information is another human. It seems that they

Five Search Strategies 99

understood search strategies to be the relationship between the information seeker

(the communicator) and the object of the information acquired (the target). They

distinguished three main types of strategies (Ramirez et al. 2002, 219 – 221):

• Interactive strategies entail direct interaction between communicator and target

during which different tactics are enabled to elicit desired information; for example,

the communicator interrogates the target, discloses information designed to elicit

reciprocal disclosure, and attempts to relax the target in order to acquire

information.

• Active strategies involve acquiring information from other individuals but without

direct interaction with the target, as is the case, for example, with the use of third-

party information sources, such as acquiring information through email exchanges

and chats with others familiar with the target.

• Passive strategies involve acquiring information about a target through unobtrusive

observation, such as being “ carbon copied ” on messages, eavesdropping on a conversa-

tion, or lurking on a listserv.

Ramirez et al. ’ s classification demonstrates that universal, or abstract, construal of

search strategies makes them independent of the technology used, and certainly free

of association with technical attributes of an information system, whether a human

or a machine.

In summary, the unsystematic nature of the use of the concept search strategy , sup-

ported by the lack of explicit understanding of the concept, created a muddled trail

of research about search strategies in which only the term itself is common to all

investigations.

5.1.2 Definitions of Search Strategy

Most explicit definitions of search strategies were universal and abstract in nature. The

most universal one was offered by Belkin and his colleagues (Belkin, Marchetti, and

Cool 1993; Belkin et al. 1995). They defined search strategies as the behaviors in which

people engage when searching for information. One might claim that this definition

is too general and actually represents the more general concept information-seeking

behavior (ISB), thus making it difficult to differentiate between the two concepts. Nev-

ertheless, using this approach, they presented four mutually exclusive dimensions (or

facets) of strategies that together create search strategies. That is, each search strategy

is a combination of elements drawn from the four facets. Each facet, in turn, includes

a continuum of elements that Belkin et al. (1993) derived from informal analysis of

empirical studies. For each dimension they listed the two extreme strategies. 5 The

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dimensions were method of interaction (from scanning to searching); goal of interac-

tion (from learning to selecting); mode of retrieval (from recognition to specification);

and resources considered (from information to metainformation). 6 These dimensions

demonstrate a very broad construal of search strategies , and raise some questions. It is

difficult to accept goal of interaction , for example, as a dimension of a strategy. A goal

may provide a reason for selecting a certain search strategy but it is not a dimension

of it. This is because strategies are usually associated with activities, whereas goals do

not represent activities and are not even directly identified by them, since various

activities may lead to the same goal and one activity may lead to the accomplishment

of more than one goal. In addition, this broad definition cannot guide researchers in

discovering other strategies, and thus limits the possible strategies to those Belkin

et al. have defined.

A definition that is universal, yet in sync with the notion of strategy in everyday

language, and the first one formulated in HIB, was offered by Marcia Bates (1981). She

explained that a search strategy is: “ An approach to or plan for a whole search. A

search strategy is used to inform or to determine specific search formulation decisions;

it operates at a level above term choice and command use ” (142). This definition is

not bounded by dimensions or technology, and places search strategies as a compo-

nent of information-seeking behavior. An example of a strategy might be: First I ’ ll try

a couple of terms, and if I don ’ t get good results, I ’ ll look for better terms either by

browsing the results or by thinking about the problem in light of what was retrieved.

Gary Marchionini (1995) construed search strategies in a similar way and also placed

the concept in an abstraction hierarchy of concepts in searching behavior, in which

each level is affected by the level above it. Marchionini ’ s hierarchy moves from the

concrete to the abstract:

• “ Moves are finely grained actions manifested as discrete behavioral actions such as

walking to a shelf, picking up a book, pressing a key, clicking a mouse, or touching

an item from a menu ” (74).

• “ Tactics are discrete intellectual choices or prompts manifested as behavioral actions

during an information-seeking session … for example, when restricting the search to

a specific field or document type in order to narrow the search results ” (74).

• “ A Strategy is the approach that an information seeker takes to a problem. Strategies

are those sets of ordered tactics that are consciously selected, applied, and monitored

to solve an information problem ” (72).

• “ Patterns are sometimes conscious but most often reflect internalized behaviors that

can be discerned over time and across different information problems and searches.

Five Search Strategies 101

Patterns may be caused by chunked strategies or tactics that people internalize though

repetition and experience ” (72). One manifestation of patterns is, for example, an

individual ’ s searching style .

Iris Xie (2007) created a similar hierarchy with an understanding of search strategies

that was more general than the previous definitions, and included the goals of a

search. She explained:

Information-seeking strategies comprise interactive intentions and retrieval tactics . Interactive inten-

tions refer to subgoals that a user has to achieve in the process of accomplishing his or her current

search goal/search task. … Retrieval tactics are represented by methods and entities with attributes.

Methods refer to the techniques users apply to interact with data/information, knowledge,

concept/term, format, item/objects/site, process/status, location, system and humans. (Xie 2007,

emphasis added)

These definitions have had an impact on other studies. Vakkari (1999), for example,

used Belkin et al. ’ s (1993) dimensions among other constructs when he analyzed how

an information problem ’ s structure (i.e., structured versus ill-structured) affects search

strategies, and Xie ’ s (2007) definitions were inspired by the approaches of Belkin

et al., Bates, and Marchionini in addition to other views. The definitions have guided

empirical studies as well. Thatcher (2006), for example, employed Marchionini ’ s hier-

archy when he investigated the search strategies that were employed by 80 study

participants. He identified 12 strategies, which he named “ cognitive search strategies, ”

including the following:

The participant went to a search engine that was known to them [ sic ]; participants used different

search engines to conduct the same search; the participant deliberately opened multiple browser

windows to conduct different searches simultaneously; the participant relied solely on hyperlinks

from the homepage to get from one webpage to another. (Thatcher 2006, 1059-1063)

Thatcher ’ s search strategies are different in nature and level of abstraction from

those identified by Marchionini, who envisioned them to be laid out on a spectrum

with opposite ends: the analytical and the browsing strategies. The analytical strategies

are “ planned, goal driven, deterministic, formal, and discrete, ” while the browsing

strategies are “ opportunistic, data driven, heuristic, informal, and continuous ”

(Marchionini 1995, 73). 7 While widely accepted (if not always correctly), the distinc-

tion between these two types of strategies is not compatible with the approach pre-

sented in this book. According to the view presented here, each search is driven by a

goal (to solve an information problem) rather than by data, regardless of the strategies

employed. In addition, every strategy is a plan. Thus, even a decision to start a search

without a specific plan (i.e., browsing) is a plan. With these conceptions, Marchionini ’ s

102 Chapter 5

definitions represent attributes of searching and surfing (see section 2.1.1.1). Since these

are two modes of acquiring information, they are dichotomous, rather than the oppo-

site ends of a spectrum.

In conclusion, definitions of search strategies are usually universal and abstract and

can guide other researchers in identifying specific strategies, whether on a conceptual

level or in empirical studies. But these definitions have had one drawback: Using them

has generated an unruly repertoire of strategies in which each researcher has employed

her own view on how to carve out strategies from an analysis of the literature or from

the data at hand. In addition, the number of search strategies is growing constantly

as new ones are discovered, usually without attempting to place them in relation to

other strategies. Most concerning is the diversity in the levels of abstraction of the

search strategies that have been generated, which ranged from the physical actions to

plans of action. 8 This inconsistency points to fundamental differences among the

interpretations of the concept. With the continually increasing number of strategies,

it is useful to find a configuration that may contain them. One promising approach

to reduce this confusion is to view a search strategy as a category of plans, general

approaches, or interactive intentions (see section 5.4).

5.2 The Conditions That Shape the Use of a Strategy

Various studies identified the conditions that shape the use of a strategy, which are

usually termed “ factors affecting the choice of search strategies. ” Some of the findings

of these studies were based on an analysis of previous studies (e.g., Vakkari 1999), and

others on empirical research (e.g., Ford, Eaglestone, and Madden 2009; Rouet 2003).

In a typical investigation the researcher selects a factor of interest and analyzes or tests

its effect. Thus, Vakkari (1999) examined the effect of the structure of the information

problem; Ford et al. (2009) looked at individual differences; and Rouet (2003) tested

the effect of task specificity and prior knowledge.

Studies of this type face various challenges. For example, the definitions that

researchers employed were unable to lead investigators to the variables that are likely

to affect the selection of search strategies. It is difficult to think about a variable that

may affect, say, the strategy “ using quotation marks ” — except for the obvious one:

whether or not a searcher is familiar with the strategy. With these definitions, research-

ers have had to use a trial-and-error approach when they select the variables to be

tested. Another challenge is the relatively large number of search strategies that were

defined by researchers. Thus, even if investigators find a variable that may affect one

strategy, the variable may leave the rest of the search strategies unaffected. Indeed,

Five Search Strategies 103

typical findings of such studies that tested an array of search strategies pointed to one

or two strategies that were affected by the tested variables but found no factors that

affected the other strategies. This way, one can state that an actor with high value on

variable X is more likely to employ category A than an actor with low values, but the

question “ Which search strategies are an actor with low value is likely to select? ”

remains unanswered. 9 Considering search strategies as a category overcomes these and

other challenges (see section 5.4.2).

5.3 Systems Designed to Support Strategies

Regardless of the definition of search strategies , most scholars agree that information

systems that support the strategies are better than those that ignore them. Yet only a

few researchers have provided systems requirements to support the strategies they

unveiled or redefined. Most systematic among these researchers were Belkin, Mar-

chetti, and Cool (1993). They methodically analyzed each strategy they had defined

to identify the problems that one may encounter when employing it. Thinking about

ways a system could alleviate the problems they identified, they generated 36 require-

ments for information systems interfaces (see section 10.3.3.1). They recommended,

for example, that a system provide a “ display of resources with explanations of link

type, ” “ direct retrieval of example information items from selected terms, ” and “ struc-

tured representation of query and search ” (Belkin et al. 1993, 330 – 331).

While Belkin et al. (1993) offered highly specific requirements, based on all the

search strategies they had identified, Bates (2007) focused on one search strategy —

browsing — and offered a much more general interface requirement. She explained that

“ [g]ood browsable interfaces would consist of rich scenes, full of potential objects of

interest, that the eye can take in at once ( massively parallel processing ), then select items

within the scene to give closer attention to. ” She also presented a model of such an

interface that was developed by Toms (2000) as an example of a good interface. 10

Both Belkin et al. (1993) and Bates (2007) offered implications for the design of

universal systems, regardless of the characteristics of the searchers. Another approach

is to focus on the searchers, identifying the strategy that would be useful to them, and

then generate design requirements based on the actors ’ information behavior. It is

unrealistic to design search systems for each individual, but it is reasonable to do so

for a particular community of actors. In this case an analyst may ask, What strategies

will play a central role in these actors ’ search for information? Once this question is

answered, implications for design could also be based on the typical characteristics of

the actors. Browsing support provided to scientists, for instance, should probably be

104 Chapter 5

different from that offered to youth looking for health information. This difference is

required not only due to the dissimilarity in the actors ’ cognitive resources and

context, but also due to the centrality of the browsing strategy for each community.

While browsing is likely to be essential to youth looking for information in an unfa-

miliar area, scientists are not likely to employ it as a central strategy. Section 5.4.3

provides a comparison between two communities ’ strategy selections and the resulting

design requirements as an example.

5.4 Search Strategy as a Category

A search strategy is cognitive in nature — because plans, general approaches, or interac-

tive intentions are all hatched in the human mind — regardless of the contextual situ-

ation that shapes it. In my work I have applied the conceptual framework cognitive

work analysis (CWA) to HIB (see chapters 11 and 12). CWA views strategies in associa-

tion with decision-making processes (see section 11.1). Vicente (1999) — based on

Rasmussen (1981) — defined a strategy as “ a category of cognitive task procedures that

transform an initial state of knowledge into a final state of knowledge ” (220).

Rasmussen, Pejtersen, and Goodstein (1994) explained that cognitive processes

within the same category — that is, the same strategy — “ share important characteristics,

such as a particular kind of mental model, a certain mode of interpretation of the

observed evidence, and a coherent set of tactical planning rules ” (70). 11 Vicente (1999)

further explained that each strategy is “ based on a different set of performance criteria,

and requires a different kind of information support ” (219).

Strategies can serve various decision processes, such as diagnosis, evaluation, or

planning (Rasmussen et al. 1994).

5.4.1 Five Search Strategies

In the area of information science, field studies in information retrieval (IR) that were

guided by CWA have defined strategies that are employed in the information search

process. 12 More specifically, Pejtersen (1984) uncovered five distinct search strategies

(Pejtersen 1979) in her study of fiction retrieval in public libraries. Later studies have

observed the use of these strategies and found no additional ones. 13 Browsing and

analytical strategies are included in this set, but their definitions are different from

Marchionini ’ s (1995). The strategies are presented in table 5.1

Although each search strategy is derived from a certain mental model, actors may

switch strategy in the middle of a search. 14 One may use a library catalog employing

the analytical strategy, for instance, to find the location of a book on a particular topic,

Five Search Strategies 105

but browse the shelf for additional sources once that book has been located. Similarly,

an actor may enter a complex search query but continue browsing through links when

the results are not satisfactory. When conducting a study of searching behavior, it is

sometimes difficult to detect a strategy shift. This difficulty is particularly the case

when the analysis is based only on observation or on transaction logs. In fact, it is

very difficult to identify search strategies without access to the cognitive processes

involved in the specific search. A transaction log of a web search may show, for

example, two terms in the search box followed by many clicks on links. Without

understanding the mental model the actor had, it is impossible to determine if he

employed the browsing or the analytical strategy. An awareness of the cognitive pro-

cesses is required for the definition of search strategies because they reflect a mental

model rather than specific procedures. Observation and analyses of transaction logs

by themselves can identify only procedures and cannot provide insight to the mental

model that is employed in a search.

5.4.1.1 The Browsing Strategy

The browsing strategy (intuitive scanning following leads by association without much

planning ahead ) had been identified long before computers began to be used for infor-

mation retrieval. Although its most commonly recognized manifestation has been

browsing bookshelves, the introduction of hypertext made browsing a highly viable

strategy when searching digital information systems. A person who decides to browse

in order to find information for making a decision might think: “ Let me start here

and see where it takes me. ” When searching the web, one might follow this decision

by clicking on links or using a directory.

Table 5.1 Search strategies and their definitions

Search strategy Definition

Browsing Intuitive scanning following leads by association without much planning ahead

Analytical Explicit consideration of attributes of the information problem and of the search system

Empirical Based on previous experience, using rules and tactics that were successful in the past

Known site Going directly to the place where the information is located

Similarity Finding information based on a previous example that is similar to the current need

106 Chapter 5

This view of browsing is different from Marchionini ’ s (1995, 73) not only in meaning

but also in type (he argued that browsing strategies are “ opportunistic, data driven,

heuristic, informal, and continuous ” ). His interpretation of the strategy is based on

the category “ elements that drive a search ” (opportunistic, data driven) and on the

category “ manner in which the search progresses ” (heuristic, informal, continuous).

That is, while all these elements that define browsing are cognitive, they belong

to different categories. In fact, according to the CWA definition, a browsing strategy

can fit in Marchionini ’ s analytical one because it can be goal driven, deterministic,

and formal.

The browsing strategy has attracted more research interest than any other strategy,

and has had the widest range of interpretations (see reviews of these in Chang

and Rice 1993 and in Rice, McCreadie, and Chang 2001). One example of a

thorough conceptual investigation into the concept is Bates ’ s (2007) question:

“ What is browsing — really? ” She placed the concept in human development and

found that “ most animals have a propensity toward exploratory behaviour. ” Viewing

browsing in the context of this behavior led her to conclude that “ browsing is a cogni-

tive and behavioural expression of this exploratory behaviour, ” and that in humans,

curiosity is “ the in-built motivation for this exploratory behaviour. ” Thus, her defini-

tion is:

Browsing is the activity of engaging in a series of glimpses, each of which exposes the browser

to objects of potential interest; depending on interest, the browser may or may not examine

more closely one or more of the (physical or represented) objects; this examination, depending

on interest, may or may not lead the browser to (physically or conceptually) acquire the object.

(Bates 2007) 15

On the empirical research front, Shan-Ju L. Chang (2005) carried out the most

comprehensive series of studies on browsing. Besides identifying the dimensions that

can support a description of browsing, 16 she created a multidimensional framework

for understanding the influences on the process as well as the consequences of

browsing.

In addition to being the most explored strategy, browsing is also the most perva-

sively used strategy in information searching. While it is a strategy on its own, it can

also occur as a sequence when other strategies are employed. Retrieving a desired book

from the library shelves, for example, requires some browsing on the shelf before the

specific book can be located. Similarly, when one finds a web site, using any search

strategy, that provides the needed information, one might click on additional links

for further exploration. Despite its prevalence, no formal training about how to browse

Five Search Strategies 107

exists (to my knowledge), 17 and search engines provide no support for the strategy, 18

as evidenced by the common lost-in-cyberspace situation.

5.4.1.2 The Analytical Strategy

Using the analytical strategy, one explores the information need on the one hand

and systems capabilities on the other. 19 The next step is to match the need and

the system ’ s attributes — or, translate the need into a query