Measuring the Affective Information Environment of Web Searchers
Diane Nahl
Library and Information Science Program, Department of Information and Computer Sciences, University of
Hawaii, 2550 McCarthy Mall, Honolulu, HI 96822 Email: nahl@hawaii.edu
Abstract
Information seeking research and theory is
focusing increasingly on the role of affect in
information behavior and how it influences
cognitive operations. Affective variables that have
been explored include need, preference, attitude,
task motivation, expected and felt effort,
uncertainty, self-efficacy, optimism, relevance,
satisfaction, and acceptance of or loyalty to the
system. This study gives operational definitions
for measuring several affective variables in the
form of rating scales filled out by college students
at the beginning and end of weekly Web search
sessions throughout a semester. Intercorrelations
and ANOVA analyses showed that there is a
dynamic and coherent interaction among these
affective variables. It is shown that the affective
environment of searchers can be monitored
objectively and continuously by means of such
measures. A new concept termed “affective load”
is introduced and defined, along with “user
coping skills” which can counteract and reduce
the negative effects of uncertainty, frustration,
anxiety, irritation and rage during searching.
Introduction
A significant new development in information seeking
research and theory is the focus on the role of affect in
information behavior (Nahl 1996b, 1997, 2003; Nahl &
Tenopir, 1996, Picard 1997, Kuhlthau 1991, Wilson, 1999;
Wilson, Ford, Ellis, Foster, & Spink. 2000). This new focus
on the affective domain, both human and computer, extends
information seeking and retrieval into the social setting, and
to the mental norms that each person develops in the form
of habits of feeling and interpreting that are inherent to
cultural group membership (Dervin 1992, Kuhlthau 1991,
Nahl, 1996a). The affective component of acquiring
Internet literacy and adaptation was shown to undergo
developmental phass that paralleled cognitive development
(Nahl and James, 1996; Nahl, 1998a). Since motivational
states and goal-directed thinking are internally ordered by
both the social values (affective) and the structure
(cognitive) of the information environment, therefore,
information science overlaps with a social-behavioral
psychology of development and habit formation (Bandura,
1986, 1995). The influence of positive affect on cognitive
processes has been shown to facilitate learning in many
areas of human endeavor (Isen, Daubman, & Gorgoglione
1987).
Nahl (1998b) shows that “affective organization is a
cultural socialization property” that people bring to the
search situation. These properties can also be referred to as
“culturally structured motivational components” that
include the need to search and find information for personal
motives. Cognitive literacy depends on this prior cultural
organization of affective goals and motives. Cognitive
information will become relevant and of interest to the
extent that it promotes the culturally organized affective
goals of each searcher. Without this affective support,
information is of no value to the individual. The searcher’s
“affective filters” (Nahl 1998b) are set to keep out or let
pass, anything that is felt to be insufficiently relevant to the
searcher’s definition of the topic area.
Chatman (in Graham, 2000) shows that affective states
such as “alienation,” “information avoidance,” and
“disinterest” have a strong influence on information
behavior in everyday contexts. Chatman useed “social
network theory” to examine how a group of culturally
homogenous older women rely on each other for
information by sharing the inventory of jointly held
attitudes and verbal slogans or explanations. The affective
model proposed in this study is also based on the idea that
socio-cultural norms regulate the private world of the
searcher at the affective and cognitive levels of operation.
Learned affective and cognitive norms, both negative and
positive, function to construct a shared world where
interaction and communication are made possible through
this commonality.
Wilson et al. (2000) proposed a new formulation of
information searching in formally recognizing that the
affective goal state imparts directionality to problemsolving steps. There is therefore a representation of the
search outcome that would satisfy the affective state or
need. This is the mechanism, according to Wilson et. al., of
the interactional dynamics between the affective and the
cognitive aspects of information behavior. One of the
significant results reported in the present study is that a
predictable set of feelings were reported by subjects during
each of the stages of uncertainty reduction and resolution.
These included: expressed feelings of uncertainty,
pessimism, dissatisfaction, confusion, frustration, self-doubt
and disappointment (as well as their opposites).
The Unit of Information Searching Behavior
It is generally accepted in the fields of psychology and
education that human behavior operates within three levels
or domains called affective, cognitive, and sensorimotor (or
“psychomotor”). Information searching behavior can be
defined within this triune classification system as a form of
goal-directed behavior in which people are motivated
(affective) to formulate a plan (cognitive) and perform it
(sensorimotor) (Nahl 1988b, 1988a),
It is also known that goal-directed behavior involves these
three domains at both the macro and micro levels of
activity. For instance, in order for searchers to begin a
search and to continue searching until something
satisfactory is found, they must maintain continuous
motivation from the beginning to the end of the search
session. If at any moment this motivation ceases, a person
stops searching and does something else. Maintaining
continuous motivation is a macro level affective behavior
(Nahl 2001). It is equivalent to the feeling of “not wanting
to quit or stop,” or the feeling of “wanting to continue no
matter what”. or even the feeling of “flow.” Motivations
and feelings are therefore the chief activators of searching,
while plans and problem solving strategies are their
resultant “formulations” in the cognitive domain. These
cognitive formulations are driven, selected, and guided by
particular affective motivations or feelings that define the
goal. This determines how searching and problem solving
are “goal-directed” behaviors. The goal provides the motive
or affective power to start the task and continue to perform
it until the goal is reached and the feeling or “drive” is
resolved and ceases.
The same mechanism operates at the micro level of search
behavior (Nahl & Jakobovits 1985). For instance, the
feeling of insistence that is sometimes expressed verbally
when a searcher says “It must be on this page. I know it is.
Where is it? Where is it?” accompanied by furious problem
solving (cognitive) and scanning up and down the page
(sensorimotor). Here too, it is the affective behavior
(feeling of insistence and urgency, refusing to give up) that
controls the frantic thoughts and the movement of the eyes
and mouse hand (when it’s a computer screen). By
definition, all information behavior, no matter how small or
partial, must be driven by a feeling-motivation, or else the
behavior stops and is replaced by another behavior driven
by some other affect or goal-feeling.
From these considerations it is clearly important to study
how the affective behavior of searchers influences their
cognitive thinking and their sensorimotor executions. This
study attempts to identify several different affective
components that have recently become prominent the in the
psychology literature, and to some extent in the information
science literature. They include self-efficacy (Nahl 1996a,
Eastin & LaRose 2000), optimism (Carver & Scheier
2001), uncertainty (Wilson et. al. 2000), time pressure
(Association for Information Systems 1998), expected
effort (Elzer 2004), task completion motivation (Gettys
2002), and expected difficulty (Tracey 1986). Not much is
yet known about how these and other affective subcomponents influence the cognitive behavior considered
both at the macro level (strategy, plan) and micro levels
(problem solving sequences). It is assumed that every
searcher operates at all three levels simultaneously. It is
also assumed that the affective level always plays the
driving and directing force which is visibly tied to a
particular goal or criterion feeling of satisfaction. It is this
affective goal and feeling of satisfaction that governs
evaluation and acceptance of the outcome of the search
activity.
This affective goal or criterial feeling selects the cognitive
operation which is most likely to lead to the satisfaction of
the goal feeling. Some cognitive plans are rejected and
some are developed further, depending on the directionality
imparted by the affective goal. For example, an affective
orientation of optimism expands the variety of strategies
considered in problem solving, while pessimism reduces the
choices and maintains inflexibility (Carver & Scheier
2001).
Operational Definitions of Affective Measures
All affective sub-components proposed in this study are
operationally defined measures. This is the generally
accepted method for investigating affective and cognitive
behavior, which can seldom be observed directly.
Operational definitions are given in terms of self-report
ratings that searchers provide at the beginning and at the
end of a search session. It is generally accepted that selfobserving one’s affective and cognitive behavior is an
ordinary ability. This is the basis of questionnaires and
personality tests, as well as expected behavior in the court
system. People are normally able to report their thoughts at
any particular moment, as well as the feelings they actively
maintain, such as degree of motivation, frustration
tolerance, preference rankings, goal hierarchies and
satisfaction scales. These are traditional methods used to
measure affective behavior.
Task Completion Motivation (TCM)
This affective component refers to the intensity of
motivation a user has for completing a search task and is
measured by two items: Q. 7. How important is this task to
you today? Not Important (1) to Extremely Important (10),
and Q. 8. How upset would you be if you found nothing
today? Not Upset (1) to Extremely Upset (10).
Uncertainty Feelings in Information Seeking (U)
Uncertainty is an affective component that accompanies
information searching and that is generally experienced as
negative when it is intense. The escalating intensity is
defined through four phases of Uncertainty: irritation,
anxiety, frustration, rage. Uncertainty is measured by
responses searchers give when asked about these four
reactions on a questionnaire. These four components are
measured by the following items: Q. 16 How irritated did
you feel in today’s search task? No Irritation (1) to
Extremely Irritated (10). Q. 17. How anxious did you feel
in today’s search task? Not Anxious (1) to Extremely
Anxious (10). Q. 18. How frustrated did you feel in today’s
search session? Not Frustrated (1) to Extremely Frustrated
(10). Q. 19. How much rage did you feel in today’s search
task? No Rage (1) to A Lot Of Rage (10).
Time Pressure (TP)
This affective component intensifies the uncertainty
component. For instance, when time pressure is low,
uncertainty may not develop beyond irritation and anxiety.
But when time pressure is high, a user is affected by
frustration and even rage. Time pressure is measured by
subtracting “Felt Length” from “Expected Length”:
TP = Expected Length – Felt Length
Expected Length is measured by this item given at the
beginning of the session: Q. 6. If you compare this task to
other search tasks you’ve done, how long should it take in
your opinion? Much Less Than Others (1) to Much More
Than Others (10). This is followed at the end of the session
by the item that measures Felt Length: Q. 24. If you
compare this task to other search tasks you’ve done, how
long did it take in your opinion? Much Less Than Others
(1) to Much More Than Others (10).
Affective Load in Searching (AL)
Affective load is defined as Uncertainty multiplied by
Time Pressure.
AL = U x TP
When affective load is relatively high in a task, the user
requires adequate coping skills to avoid giving up on the
task, and to avoid experiencing feelings of dissatisfaction or
failure. When affective load is low, the user is not as
challenged by the negative consequences of uncertainty.
Self-Efficacy Feeling as a Searcher (SE)
This affective component measures searchers’ trust in
their competence to succeed in the task. Searchers with
higher SE scores have more effective coping skills. Self
Efficacy (SE) is measured by three items given at the
beginning of the session: Q. 9. How sure are you that you
will succeed in this task? Doubtful (1) to Almost Certain
(10). Q. 10. How likely is it that you will become good at
this type of task? Pretty Doubtful (1) to Almost Certain
(10). Q. 11. How much luck do you have in searching in
comparison to other types of tasks? I Have Bad Luck (1) I
Always Find Something Useful (10).
Search Optimism Feeling (Op)
This affective component measures a searcher’s
motivation to postpone quitting in the face of time pressure
and uncertainty. If the affective load (AL) for task
performance is too high, searchers will terminate the task.
Optimism provides the motive to oppose giving up under
pressure and challenge. Searchers who have high Optimism
scores make use of more effective coping skills, e.g.,
persistence and perseverance in their task completion
motivation (TCM). Search Optimism (Op) is measured by
the following three items given at the beginning of the
session: Q.12. How motivated are you to keep on trying
today until you succeed? Slightly Motivated (1) to Very
Highly Motivated (10). Q. 13. Computers and search
engines make it easy for people to find what they’re looking
for. I Strongly Disagree (1) to I Very Much Agree (10). Q.
14. How likely is it that there will be something specific on
what you’re looking for? Not Likely (1) to Very Likely
(10).
User Coping Skills (UCS)
This affective component is defined by two subcomponents acting together: Self-Efficacy (SE) and Search
Optimism (Op).
UCS = SE + Op
These two affective components are measured by selfratings at the beginning of a search session and with content
analysis of comments recorded during sessions. The higher
the total UCS score, i.e., the better the coping skills, the
more effectively a user can manage uncertainty, and hence
reduce affective load (AL). Effective coping skills act to
eliminate uncertainty which routinely arises during task
performance. Every ordinary user brings some coping skills
to a search task, but there are individual differences due to
motivation, experience, and literacy. User coping skills are
learned affective responses to information behavior
management.
For example, when users press a wrong key and realize it,
they may use various verbalizations as a sign of their
negative emotions (anger, self-criticism, wasting time):
“Oops!” “Oh, no!” “What did I do?” “Oh, I hit the wrong
key!” “Stupid me!” etc. These overt behavioral expressions
indicate the activity of inner emotions that can be quite
intense, causing some users to walk away, to make a fist, to
bang on the keyboard, to swear, and other forms of hostility
and affective urgency. Though users experience the
intensity of these emotions, they are expected not to
“overreact” hence, to control their overt behavior. This
behavioral self-control is accomplished through the coping
skills that the individual has brought to the search session.
Self-efficacy (SE) provides the motivation necessary to
oppose self-criticism and self-doubt that are activated by
uncertainty and temporary failure (Nahl 1996a). Similarly,
optimism (Op) provides the motivation to enlarge the range
of cognitive activity in problem-solving, leading users to try
different things when discouraged or facing an obstacle.
There are other user coping skills (UCS) that need to be
researched in the future.
Evaluation of Search Episodes
This affective component is a measure of the extent to
which users feel that they have met their search goal for the
task. Since goals for a task may be complex and multiple,
several sub-components must be measured.
Worthwhile = Expected Effort - Felt Effort
Expected Effort (E Eff) is an affective component
measured by self-ratings prior to beginning the search task.
The affective component of Felt Effort (F Eff) is measured
at the completion of the task or the end of the search
session by means of self-rating. If the Expected Effort (pre)
is greater than the Felt Effort (post), users feel that the time
and effort were Worthwhile (W)—a positive feeling of
reward. But if the felt effort is greater than the expected
effort, there is an accompanying feeling of dissatisfaction or
ambivalence.
Expected Effort (E Eff) is measured at the start of the
session by this item: Q 15. How much effort do you expect
this task to take today? Not much (1) to A Tremendous
Amount (10). Felt Effort (F Eff) is measured at the end of
the session by this item: Q. 20. How much effort did this
task require of you today compared to other search tasks in
your experience? Much Less Than Others (1) to Much
More Than Others (10).
Evaluation (Ev) is defined by two sub-components:
Worthwhile and Relevance. Future research should
investigate additional affective sub-components. In this
study, Satisfaction is defined by the sum of Worthwhile
(W) and Relevance (R).
Worthwhile + Relevance = Satisfaction
Relevance (R) is measured by users’ rating of the search
results on a scale of relevance to what they had been hoping
to find.
Acceptance of Search Environment
This affective component (Acc) measures the amount of
support the user feels for the system environment through
which the search was performed. It is measured by these
two items given at the end of the session: Q. 22. How
supportive are you of the search engine or computer facility
you used today? Not supportive (1) to Very Supportive
(10). Q. 23. How easy was it to use the search engine or
computer facility today? Very Easy (1) to Very Difficult
(10).
Design
The data are based on rating forms filled out by senior
college students enrolled in a “Writing Intensive”
psychology seminar with several required Web research
reports on assigned topics (e.g., noise, aggressive driving,
spirituality, La Femme Nikita, etc.). The rating form
included 26 questions in the following format:
18) How frustrated did you feel in today’s search session?
1
2
not
frustrated
3
4
5
6
7
8
9
10
extremely
frustrated
The affective measures in this study are based on these
ratings. Part of the form was filled out at the start of each
session and part at the end so that pre-post comparisons can
be made, as explained below in the definition and the
content of each rating. The form itself is available on the
Web: www2.hawaii.edu/~nahl/forms.html
Students (N=73) kept track of each search session with
the form and submitted the filled-out forms with their
reports. Most students submitted 10 forms (for 10 Web
search sessions performed throughout the semester) but
some submitted fewer (range=6 to 10). The data are based
on the mean of each student’s ratings as this was considered
a stable measure of each individual’s regular search habits.
Results
Table 1 identifies and defines the affective measures used,
and gives the group mean and range for each variable. Each
rating scale has a maximum of 10 and when the measure is
based on two scales the maximum would be 20, and so on.
Table 1. Affective Measures Used
Variable and
Symbol
Definition
(maximum for each scale
= 10)
Mean
(N=73)
and
(Range)
Acceptance (Acc)
Search engine support +
ease
11
(5 to 20)
Affective Load
(AL)
Uncertainty intensified by
time pressure (U x TP)
123
(18 to 369)
Evaluation (Ev)
Acceptance + Satisfaction
(Acc + S)
19
(7 to 30)
Expected Effort
(Ex Eff)
Expected effort at
beginning
6
(1 to 9)
Felt Effort (Felt
Eff)
Felt effort at end
5
(1 to 9)
Optimism (Op)
Keep trying + Good search
engines + Lots of info
25
(16 to 30)
Relevance (Rel)
Rating of results
8
(2 to 10)
Satisfaction (Sa)
Worthwhile + Relevance
(W + Rel)
9
(-1 to +16)
Self-Efficacy (SE)
Sure of success + Getting
good at + Good luck
25
(18 to 30)
Task Completion
Motivation (TCM)
Importance + Getting upset
Time Pressure (TP)
Expected Length – Felt
Length
10
(2 to 19)
Uncertainty (U)
Irritation + Anxiety +
Frustration + Rage
12
(4 to 27)
User Coping Skills
(UCS)
Self-efficacy + Optimism
(SE + Op)
50
(35 to 60)
Worthwhile (W)
Expected effort at start –
Felt effort at end (Ex Eff Felt Eff)
0.49
(-7 to +6)
13
(4 to 20)
In the next analysis a correlation matrix was examined to
see the patterns of inter-correlations between the affective
sub-components. For the four scales that measure
Uncertainty (U) the inter-correlations were highly
significant, ranging from .43 to .88. Correlations of each
scale with the total U ranged from .76 to .90 indicating that
the four rating scales each contribute their particular subvariety of affect to produce a stable Uncertainty score.
Other correlations reveal to some extent how the subcomponents of the affective environment interact to
produce the searcher’s actual experience. For instance,
Relevance (Rel) ratings of the results correlate significantly
with Satisfaction (S) (r=.77), with Evaluation (Ev) (r=.74)
and with User Coping Skills (UCS) (r=.48). In other words,
searchers who have higher user coping skills (i.e., high selfefficacy as a searcher and optimism for search success), are
more satisfied, give better evaluations of the system, and
rate the results as more relevant.
User Coping Skills (UCS) are positively related to
Relevance and Evaluation (.48 and .34 p<.05) but
negatively related to Affective Load (AL) (-.23, p=.05). In
other words, self-efficacy and optimism work to reduce
affective load, though further research needs to examine the
conditions that influence the strength of this relation, which
in this case is very weak. Affective Load (AL) correlates
negatively with coping skills (-.23) but positively with Time
Pressure (TP) (r=.55) and Uncertainty (TP) (r=.89). This
indicates that time pressure and uncertainty are direct
contributors to affective load in a search session.
Expected effort (Ex Eff) at the beginning of the session is
significantly correlated with rated importance of the search
task (r=.51) and how long prior searches took relative to
this one (r=.52). In other words, searchers who rate their
task as more important are also prepared to put more effort
into it. Feeling supportive of computers is correlated with
agreeing that computers and search engines make it easy to
find something (r=.74).
These highly significant intercorrelations lend support to
the validity of the measures since these affective subcomponents are consistent with each other.
According to the affective model proposed here, a key
dynamic in the affective environment of searchers is how
they balance negative and positive affect generated by the
search task. Self-efficacy and Optimism generate positive
affect and are defined together as User Coping Skills (i.e.,
UCS = SE + Op). The mean for the group was 50, with a
range of 35 to 60. Since there are six rating scales involved
(see Table 1), the maximum score is 60. By dividing the
group at the mean of the UCS measure (above and below
50), we obtain two independent sub-groups called “High”
(N=39) and “Low” (N=32), with no overlapping scores
regarding their ability to cope with negative affect (as
measured by Uncertainty (U) – see Table 1). According to
the proposed model, searchers who have higher affective
coping skills should be able to better manage the negative
affect they experience (frustration, irritation, etc.) and feel
more positive about their results and overall experience
(evaluation, satisfaction, self-efficacy and optimism).
Table 2. Consequences of Higher vs. Lower
User Coping Skills (UCS)
Affective Variables
Group
with
higher
coping
skills
(N=39)
Group
with
lower
coping
skills
(N=32)
Acceptance
11.8
11.0
n.s.
Affective Load (AL)
118
129
n.s.
Evaluation (Ev)
20.5
18.3
p<.0001
Expected Effort at
start (Ex Eff)
5.7
5.6
n.s.
Felt Effort at end
(Felt Eff)
5.0
5.3
n.s.
Optimism (Op)
27.0
22.7
p<.0001
Relevance of results
(Rel)
8.7
7.4
p<.0001
Satisfaction (Sa)
9.3
7.7
p<.0023
Self Efficacy (SE)
27.3
22.7
p<.0001
Task Completion
Motivation (TCM)
14.0
12.5
p<.05
Time Pressure (TP)
10.1
10.5
n.s.
Uncertainty (U)
11.6
11.8
n.s.
Worthwhile (W)
0.65
0.30
n.s.
(see Table 1 for
definitions)
Significance
Level
The overall pattern of ANOVA results shown in Table 2
indicate some significant support for the model. Searchers
with higher coping skills do better on all comparisons and
the differences are highly significant for half of the
comparisons. In further research user coping skills can be
measured independently of Self-efficacy and Optimism to
verify that the results are stable across independent
measures. Task completion motivation is significantly
higher for the higher coping sub-group. In other words
searchers who have less self-efficacy and optimism have
weaker motivation for completing the task. Searchers who
believe in their search ability (self-efficacy) and are
optimistic about the outcome, are more motivated to keep
searching until they find something relevant. As well,
searchers with higher affective coping skills are
significantly more positive in their evaluation and
satisfaction, and they feel that they are more successful,
rating their results more relevant than searchers with lower
coping skills.
Additional ANOVA analyses were conducted to reveal
the interaction effects of the affective components studied
in this investigation. In one question, searchers were asked
to indicate “which factor was most important in getting the
search results: ”luck”; “my search techniques”; ”a
combination of luck, search techniques, and the search
engine.” Participants were distributed almost evenly in
these three categories or sub-groups. Note that this is a
cognitive rather than an affective measure. Is there a
difference in affective behavior in relation to these
cognitive beliefs? The ANOVA in Table 3 compares the
affective behavior of the three independent sub-groups.
Table 3. ANOVA for Three Sub-groups
Affective
Variable
Subgroup 1
“my
search
techniques”
Subgroup 2
“quality
of the
search
engine”
Subgroup 3
“combination of
things
including
luck”
Significance
Level
Uncertainty
(U)
8.9
12.1
12.9
p<.04
Affective Load
(AL)
83.9
123.2
142.9
p<.03
User
Coping
Skills
(UCS)
51.8
47.6
50.1
p<.08
Table 3 indicates that critical affective differences in
behavior are associated with certain cognitive behaviors of
searchers. People who attribute success to their own search
skills experience significantly less uncertainty and affective
load than those who attribute success to uncontrollable
factors outside of themselves.
Discussion
Future research will attempt to replicate these findings on
a new group using the same rating form and more difficult
search tasks. The students in this study generally had high
positive affect and low uncertainty levels (see Table 1).
More studies need to explore the affective dynamics under
a more challenging set of conditions to determine the
validity of the concept of “affective load” and “user coping
skills.” Once a stable instrument is achieved and affective
dynamics are better understood, it will be possible for
information managers to monitor the affective environment
of users on a routine basis. Support and counseling
interventions can be triggered when affective load is rising
above a specified level (Nahl 1997). Knowledge about the
affective environment of searchers will also be helpful in
instruction for searchers and in system design.
Finally, more research is needed on how the affective
information environment of searchers impinges on their
cognitive activity. According to Kuhlthau (1993)
uncertainty leads users to be less willing to continue
searching or interacting with a system. Back and
Oppenheim (2001) state that uncertainty can be considered
to be one of the components that contributes to cognitive
load. In this study, Uncertainty (U) was operationally
defined by affective measures and it was shown to add to
affective load when coping skills were inadequate. There is
therefore a mutual interaction between affective load and
cognitive load that needs to be further studied.
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