Ordering Matters: An Experimental Study of Ranking Influence on
Results Selection Behavior during Exploratory Search
Emanuel Felipe Duarte
1
, Lucas Pupulin Nanni
1
, Ricardo Theis Geraldi
1
, Edson OliveiraJr
1
,
Val´eria Delisandra Feltrim
1
and Roberto Pereira
2
1
Informatics Department, State University of Maring´a, Maring´a-PR, Brazil
2
Institute of Computing, State University of Campinas, Campinas-SP, Brazil
Keywords:
Search User Interfaces, Exploratory Search, Ranking Influence, Relevance Judgement.
Abstract:
The design of Exploratory Search tools acquires more importance as the amount of information on the Web
grows. Accordingly, informed design decisions concerning the users’ behavior during search activities can
be used in novel approaches for Exploratory Search tools. With regard to users’ behavior on search result
selection, literature indicates higher ranked results tend to attract more attention and, therefore, more hits.
However, the cause of such behavior is not clear. We experimentally investigate the hypothesis of this behavior
being due to ranking. A group of 72 participants was asked to select a search result from a randomly ordered
results list. The experiment was carried out on paper to remove specific search engine and digital medium
biases. We obtained evidence indicating there is a trend towards choosing higher ranked results even in a
different medium and in the context of an Exploratory Search, corroborating to the hypothesis that ranking has
influence on users’ selection behavior.
1 INTRODUCTION
According to White and Roth (2009), exploration is
part of the human nature and, as explorers, we aim
to constantly expand our knowledge through explo-
ration. Blandford and Attfield (2010) discuss that
continuous advances in information technology over
the past decades and, in particular, the advent of the
Web have revolutionized the way people interact with
information. Consequently, the amount of available
information to be explored has become abundant.
However, Blandford and Attfield (2010) discuss
how the abundance of information, most of it located
on the Web, became a significant problem in recent
years. According to White and Roth (2009), there is
a growing need for Interactive Information Retrieval
systems capable of safeguarding the user’s attention
through information filtering.
White et al. (2005) argued search technologies
available at that time already provided adequate sup-
port for users with well-defined information needs,
but lacked support for situations where users do
not have the knowledge or contextual awareness to
formulate queries or navigate complex information
spaces (Bates, 1989). According to White et al.
(2013), current search technologies still provide in-
sufficient support for this kind of activity, denom-
inated Exploratory Search: an activity where the
user performs a search with open and abstract goals
and need to build knowledge about a particular sub-
ject. Saving users’ attention is critical in Exploratory
Search activities, therefore, there is a growing need
for models and tools that support users in Exploratory
Search activities.
Current major Web search engines, such as
Google, Bing, Yahoo! and Baidu, adopt the listing
format of ranked search results in their user interface.
In this schema, studies reveal the results that appear
at first positions of the list tend to receive most of the
attention and, therefore, more hits than the results pre-
sented at last positions (Granka et al., 2004; Joachims
et al., 2005; Pan et al., 2007).
However, it is not clear whether the position itself
is enough to influence users to select the first results.
The interaction possibilities of the digital medium
(e.g., scrolling, clicking, opening in a new tab) along
with the additional tricks used by traditional search
engines (e.g., graphical signs, additional result infor-
mation, highlights for sponsored links) might also in-
fluence results selection.
Understanding the user’s behavior towards search
activities may provide basis for informed design deci-
Duarte, E., Nanni, L., Geraldi, R., OliveiraJr, E., Feltrim, V. and Pereira, R.
Ordering Matters: An Experimental Study of Ranking Influence on Results Selection Behavior during Exploratory Search.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 427-434
ISBN: 978-989-758-187-8
Copyright
c
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
427
sions in search user interfaces, including Exploratory
Search tools in particular. Among the myriad of in-
volved behaviors, the user’s decision of selecting a
search result plays an important role in the presenta-
tion of results. The way results are presented in the
user interface should always be adapted to the user’s
behavior, promoting access to most relevant docu-
ments for a given information need. In Exploratory
Search, for instance, this behavior has a stronger in-
fluence on the activity outcome, since the first results
alone are usually not enough to satisfy the informa-
tion need. If the first results are chosen with not
even regarding their relevance, the listing presenta-
tion might not be the best choice for the design of Ex-
ploratory Search tools, since low ranked results may
not be visited even if they are more relevant to the
user’s information need. Therefore, it is necessary to
investigate and better understand users’ behavior at
result selection regardless the web search tool in use.
In this paper we present an experimental study
with 72 participants that analyzes the user’s decision
on which search result to access first during an Ex-
ploratory Search activity. This study was conducted
to examine whether the behavior of selecting the first
results would remain in a situation where the results
were presented on other medium than a Web search
engine. A paper sheet presenting all the results in
the same page for easy comparison and selection is
the ideal artifact for this experiment because it is a
medium in which users’ attention tends to be more
equally distributed between its contents. Further-
more, it does not introduce other graphical elements
or interaction possibilities that may influence users’
perception of relevance and disturb their attention.
The paper is structured as follows: in Section 2 we
briefly present and discuss the literature review and
some related work; in Section 3 we detail the experi-
mental study considering a structure with definition,
planning, operation, results analysis and interpreta-
tion, and evaluation of experiment validity; in Section
4 we present and discuss the main results; finally, in
Section 5 we present the main conclusion and direc-
tions for future research.
2 LITERATURE REVIEW AND
RELATED WORK
According to Hearst (2009), users rarely look beyond
the first page of search results. If they do not find im-
mediately what they want on the first page, the most
common behavior is to give up on those results and
reformulate the query. Furthermore, studies suggest
that Web users expect the best answer to be in the first
or second position, and this expectation has influence
on the result accessing behavior.
In a study by Granka et al. (2004) with 26 partici-
pants and 397 queries, it was found the first search re-
sult was selected approximately 56% of the time, and
the second about 14%. It was concluded the first two
search results are considerably the most viewed and
accessed, accumulating about 70% of all accesses.
From the third result onwards there is a dramatic de-
crease in not only access, but also on visualization
(assessed by eye tracking).
In study by Joachims et al. (2005) with 29 partic-
ipants, it was found the probability of a search result
being accessed was 45% for the first one, 17% for the
second, 11% for the third and 5% or less for the subse-
quents, accumulating about 73% of all accesses in the
first three results. Such behavior persisted even for 5
participants whose results were presented in reversed
order, and also for other 5 participants where only the
first two results were reversed. An additional analysis
comparing the access frequency of the first and sec-
ond results with a previous relevance judgment indi-
cated participants still prioritized the first result even
though its relevance was previously assigned inferior
compared to the second result.
A divergent behavior was observed in a study car-
ried out by Aula et al. (2005) with 42 students and 10
pre-defined queries. They found the presence of two
different styles of visual exploration over search re-
sults: (1) nearly 46% of the participants were found,
in the words of the authors, “economic”, visualizing
only 3 or 4 results at most in 50% of the tasks; and
(2) the other 54% were found, also in the words of the
authors, “exhaustive”, visualizing more than half the
results, and in some cases even all the 10 results, for
the most of queries. The authors suggest that more
experienced users are more likely to adopt the “eco-
nomic” profile, while less experienced users are more
likely to adopt the “exhaustive” profile. Thereby, they
argue the study conducted by Granka et al. (2004) has
employed only experienced searchers, which would
explain the difference in results between studies.
Another factor to be considered is the effect
caused by trust in search engine brand. As shown
in studies by Jansen et al. (2009) and Jansen et al.
(2012), users usually show a bias toward trusting
some search engines. Participants were presented
with the same results in different search engines, and
attributed more relevance for the results from their fa-
vorite search engine. As another example, in a study
carried out by Pan et al. (2007), users tended to trust
and select the first results from Google even though
their snippets were considered less relevant than fol-
lowing results.
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
428
The above studies identify the users’ behavior dur-
ing the analysis and selection of search results re-
trieved by a search engine. They show higher ranked
results attract more attention and, therefore, have a
higher chance of being accessed. In addition, the
user’s experience with search also contributes to the
depth of results exploration, in which less experi-
enced users tend to go further in the list whereas
more experienced users tend to stick to the first re-
sults. However, such studies do not allow to con-
clude whether the behavior exhibited by users is a
ranking consequence (in the sense of the presentation
model, not the trust in the search engine ranking algo-
rithm), a medium consequence (eye tracking analysis
by Granka et al. (2004) show not every result receives
the same attention) or a combination of both.
In an earlier study with 78 participants sepa-
rated in two experiments, Eisenberg and Barry (1988)
found that the order in which documentsare presented
has influence on the user relevance judgment. The
authors verified that when the documents were pre-
sented to participants in a high to low relevance rank-
ing, they consistently underestimated the significance
of documents at the end of the list. In a low to high
situation, there was overestimationof documents, par-
ticularly in the first middle range. These results may
have been influenced by the medium: the documents
were presented in paper sheets, one document at a
time, and participants were not able to return to pre-
vious analyzed documents.
In the studies we presented in this section, par-
ticipants’s attention is not distributed equally across
the documents, either on account of the media or the
manner in which documents are presented. Hence, a
similar study where the participants’s attention tend
to be evenly distributed between the presented search
results might provide useful information.
3 EXPERIMENTAL STUDY
We (1) scoped, (2) planned, (3) operated, (4) analyzed
and interpreted the results and (5) evaluated the ex-
periment validity according to the experiment struc-
ture suggested by Wohlin et al. (2012). This structure
was chosen because it describes systematically how
to conduct and evaluate experiments not only in Soft-
ware Engineering (its original purpose), but also in
Computer Science in general. Each step is described
in this section.
3.1 Experiment Scoping
We applied the Goal Question Metric (GQM) tem-
plate by Basili et al. (1994) and expanded by
Van Solingen and Berghout (1999) to structure, con-
duct and summarize the experiment’s nature as well
as the main aspects involved.
The following description about the experiment
objective was elaborated: we are interested in ob-
serving and analyzing users’ behavior when select-
ing a search result from a list, removing possible ad-
ditional influences conventional search engines may
exert; for the purpose of understanding relevant as-
pects of user behavior during Exploratory Search ac-
tivities, which can be applied in informed design de-
cisions for Exploratory Search tools; with respect to
ranking influence in search result selection behavior
shown by users; from the viewpoint of the design of
Exploratory Search tools; and in the context of un-
dergraduate and graduate students of Computer Sci-
ence and Informatics performing Exploratory Search
activities.
3.2 Experiment Planning
Experiment planning was composed of five main
steps. In general terms, we: (1) elaborated the hy-
potheses to be tested; (2) planned the search results
selection; (3) selected the experiment participants;
(4) defined the experiment variables; and (5) elabo-
rated the experiment instrumentation. Each step is
described as follows.
3.2.1 Hypotheses Formulation
The following hypotheses were elaborated and tested:
Null Hypothesis (H
0
): Users are not influenced
by the search results ranking when selecting re-
sults to access.
Corr(SampleDist,StandardDist) = 0 (1)
Alternative Hypothesis (H
1
): Users are nega-
tively influenced by the search results ranking
when selecting results to access.
Corr(SampleDist,StandardDist) < 0 (2)
Alternative Hypothesis (H
2
): Users are posi-
tively influenced by the search results ranking
when selecting results to access.
Corr(SampleDist,StandardDist) > 0 (3)
In Equations 1, 2 and 3, Corr is a correlation
function. SampleDist is the access distribution ob-
served for each position in the search results list and
StandardDist is the access probability for each posi-
tion in the results list, according to the related work of
Granka et al. (2004) and Joachims et al. (2005). These
two studies are used since the data can be compared
with the data collected in this experiment.
Ordering Matters: An Experimental Study of Ranking Influence on Results Selection Behavior during Exploratory Search
429
3.2.2 Planning of Search Results Selection
To perform the experiment in a consistent manner
with an Exploratory Search activity, we defined an
information need compatible with this type of activ-
ity and specified it as a query. Such compatibility is
grounded on Exploratory Search attributes listed by
Kules and Capra (2009) and expanded by Wildemuth
and Freund (2012). The information need and query
were presented to the participants so that they could
identify the search context and associate it to the pre-
sented search results. The information need and the
query employed in this study are (translated from Por-
tuguese):
Information Need: Suppose you want to enter to
the job market with a good job. A large, well-
known and praised company in your area is hiring
promising professionals and invited you for a job
interview. As you would like so much to get the
job, but have never taken part in a interview be-
fore, you want to know more about how job inter-
views are conducted and how to get prepared to
succeed and get the job.
Query: “how to get prepared for a job interview”.
We issued the query to Google. This information
was omitted from the participant to avoid a possible
confidence bias on the search engine brand. We re-
trieved a list consisting of 100 search results and pre-
selected an aleatory sample of 10 results. We eval-
uated the relevance of these 10 pre-selected results
by their snippets (title, URL and description), and
they all were considered relevant to the information
need. Then a set of 200 random configurations of the
10 pre-selected results was created from its random-
ization. The generation of random configurations al-
lowed to neutralize the search engine intrinsic ranking
factor, disassociating the result potential quality from
its ranking order. Therefore, the behavior of results
selection could be evaluated only according to is po-
sition in the ranking.
Participants were asked to select the search result
likely to be the first one accessed by them in a natu-
ral search condition. Only one search result must be
selected since, at this time, we were interested only
in the behavior of the first selection, which could be
compared with the literature.
3.2.3 Participant Selection
Participant selection included students engaged daily
in Exploratory Search activities. For viability reasons,
we selected undergraduate and graduate students of
Computer Science and Informatics during class with
the permission of their professor. Every student in
class was invited to participate and there was no re-
jection. In addition, everyone authorized the use of
their experimental data and there was no exclusion of
participants after the data was collected. This pro-
cess resulted in a sample of N = 72. Participation
was voluntary, unpaid, with the possibility to leave
the study at any time with no justification. The par-
ticipants were not randomly selected due to viability
reasons. However, the assignment of search results
lists was randomized among the participants, charac-
terizing this study, according to Wohlin et al. (2012),
as a quasi-experiment.
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Figure 1: A fragment exemplifying how the results are pre-
sented to the participants.
3.2.4 Variable Definition
The following variables were defined: (1) a list of 10
search results randomly sampled (independent, as it
represents the input, the possible cause for the anal-
ized effect); and (2) the ranking influence rate on
users’ search result selection behavior (dependent, as
it represents the output, the analized effect).
3.2.5 Instrumentation
The objects used in the experimentation process were:
a document describing the experiment, the gen-
eral context of the proposed information need and
the query issued to obtain the search results, along
with a consent form;
a form containing a checklist with 10 search re-
sults in random order, in which, checking a re-
sult means the participant would access it. This
instrument does not contain any search user inter-
face element other than the results themselves and
selection checkboxes, as shown in Figure 1; and
a participant characterization questionnaire ask-
ing educational level and experience with search.
Regarding experience with search, those were the
categorization options: (i) very experienced: I
know how to use advanced search features such
as logical operators and filters, and I can find any-
thing I search for; (ii) experienced: I am not fa-
miliar with advanced search features, but I do per-
form searches daily and I easily find what I need;
(iii) intermediate: I perform searches daily, but I
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
430
do not always find what I need; and (iv) inexpe-
rienced: I only perform searches casually, and I
have difficulty knowing how to search properly.
The independent variable (list of 10 search results
randomly sampled) was measured from the partici-
pant’s selected item in the checklist. The dependent
variable (ranking influence rate on users’ search re-
sult selection behavior) was measured from the cor-
relation between the user’s behavior observed in re-
lated studies and the curve generated from the selec-
tion made by the participants.
3.3 Operation
We prepared and presented the experiment instru-
ments (Section 3.2.5) to the participants, who became
aware of the information need and proceeded to se-
lected the first search result they potentially would ac-
cess in the given context. Participants were instructed
to check only one search result in a way as natural
as possible. To promote a natural behavior they were
told there was not a “correct” answer. The experiment
was performed individually by every participant, with
no external aid and time limit. Since we did not detect
any kind of cheating or improper answering during
the experiment, the obtained results were considered
valid for the purpose of this study.
3.4 Analysis and Interpretation
We collected and summarized the data to identify the
total number of hits for each search result position
in the list. Data concerning the participants’ char-
acterization, consisting of their education and their
experience with search was also accounted for. In a
sample of N = 72 participants, 65 of them (90.28%)
are undergraduate students and 7 (9.72%) are gradu-
ate students. Regarding the experience with search, 8
(11.11%) participants claimed to be intermediate, 46
(63.89%) experienced and 18 (25.00%) very experi-
enced. No participant claimed to be inexperienced.
For purposes of comparison, the data analysis
requires a distribution to be used as a referential.
Therefore, we established an equation based on data
obtained by Granka et al. (2004) and as well by
Joachims et al. (2005). The equation is based on the
decay of access probability according to the search
result position. A curve that represents this behavior
can be described as StandardDist= a/x, such that the
area under the curve in the interval [1;10] is equal to
1, the accumulated probability for the search result
selection. This constraint can be written as:
Z
10
1
a
x
dx = 1 (4)
Solving Equation 4 we find a= 1/ ln10. Thus, the
probability curve can be rewritten as:
StandardDist =
1
xln10
, (5)
where x is the position in the search results list and
x [1;10].
We validated this curve by performing the correla-
tion between its points (StandardDist) and the distri-
bution obtained in the experiments by Granka et al.
(2004) (which we call GrankaDist) and Joachims
et al. (2005) (which we call JoachimsDist). The cor-
relation statistic was applied because it is able to di-
rectly measure the relationship significance between
two ranked distributions, which can be interpreted by
a predefined scale. As the three sets of values are non-
normal distributions (according to Shapiro-Wilk test
with a significance level of 1%), the Spearman corre-
lation test was applied with a correlation range pass-
ing through 1.0 (perfect negative correlation), 0.0
(no correlation) and 1.0 (perfect positive correlation).
Values obtained for the Spearman correlation for the
distributions were, both with significance level equal
to 0.1%:
Corr(GrankaDist,StandardDist) = 0.8842
Corr(JoachimsDist,StandardDist) = 0.9232
These values indicate the StandardDist curve is
consistent with previous studies, showing a strong
positive correlation. Therefore, the curve can be used
as a reference in correlation with the results sampled
in this study.
In Figures 2, 3 and 4 we illustrate the graphics
generated for the performed correlation. In Figures 2
and 3 the data proximity to the curve StandardDist is
clearly visible, which is plausible since the positive
correlation is strong. Although the correlation illus-
trated in Figure 4 is not clearly visible, the Spear-
man correlation (employed since the study sample
comes from a normal distribution and StandardDist is
a non-normal distribution, according to the Shapiro-
Wilk test with significance level of 1%) shows there
is a strong positive correlation, with significance level
equal to 1%:
Corr(SampleDist,StandardDist) = 0.7693
Therefore, this correlation value allows the null
hypothesis (H
0
) and the alternative hypothesis (H
1
) to
be rejected with a reliability of 99%, and the alterna-
tive hypothesis (H
2
) to be accepted, indicating users
are positively influenced by the search results ranking
during selection since:
Corr(SampleDist,StandardDist) > 0
Ordering Matters: An Experimental Study of Ranking Influence on Results Selection Behavior during Exploratory Search
431
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5 6 7 8 9 10
Percentage of accesses
Search result position
GrankaDist
StandardDist
Figure 2: Correlation between GrankaDist and
StandardDist distributions.
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5 6 7 8 9 10
Percentage of accesses
Search result position
JoachimsDist
StandardDist
Figure 3: Correlation between JoachimsDist and
StandardDist distributions.
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5 6 7 8 9 10
Percentage of accesses
Search result position
SampleDist
StandardDist
Figure 4: Correlation between SampleDist and
StandardDist distributions.
3.5 Validity Evaluation
Regarding the conclusion validity, the sample size
(N = 72), although larger than those of related work,
may not be sufficient to represent the population of
students who perform Exploratory Search activities.
The main risk for the conclusion generalization is
that all the participants are students from the IT area
in the same institution. Replications of this experi-
ment should consider participants from other areas of
knowledge, from different institutions and cultures.
Regarding the construct validity, there was a great
care for the participants to understand correctly what
should be done in the experiment. The information
need was described and explained clearly and suc-
cinctly, and the task to be performed by the partici-
pants was simple enough given their education. No
participant had difficulties in understanding and car-
rying out the experiment.
Regarding the experiment internal validity, the
following threats were considered:
Answers Accuracy: each participant selected
only one search result. They were requested to
select the first result they would access on a real
search situation; it was a simple activity that ev-
eryone easily understood, so the responses were
considered accurate.
Fatigue Effects: the experiment, including expla-
nation, lasted approximately 15 minutes and the
requested task did not require any form of exces-
sive cognitive load. Thus, fatigue effects were not
a problem.
Influence Among Participants: the experiment
was supervised by the authors and there was
no exchange of information between participants
during the study. Therefore, the influence among
participants was not a problem.
Participants Diversity: the study sample can not
guarantee this behavior also holds for people from
other areas of knowledge and other cultures. As
pointed out by Weinschenk (2011), culture can af-
fect how people think. Western cultures (the ex-
periment case) have a tendency to focus on in-
dividual elements, whereas eastern cultures have
a tendency to focus on context and relationships.
Furthermore, the predominant amount of users
who consider themselves very experienced or ex-
perienced (88.89% in total) may have influenced
the observed behavior due to the “economic” pro-
file commonly adopted by this kind of user (Aula
et al., 2005).
Regarding the experiment external validity, the
following threats were considered:
Artificial Task: Russell and Grimes (2007) argue
that artificial search activities do not accurately re-
flect the behavior of natural search activities, thus,
to “impose” a search activity for the participant
is not an ideal approach. The authors, however,
admit that artificial search activities are necessary
due to research viability, and that they can provide
useful information.
Atypical Media: in this study, the digital media
on which searches actually occur was replaced by
the printed medium. As a consequence, the be-
havior of comparison and selection was different
in terms of scan and selection time. However, al-
though the printed medium is not the natural con-
text of a search activity, the choice was deliber-
ate so that participants could distribute their atten-
tion more evenly between the search results, and
were not influenced by graphical elements and in-
teractions possibilities offered by traditional web
search engines.
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
432
4 RESULTS DISCUSSION
The correlation found in the present study (0.7693)
was not as high as the correlation found in the other
two distributions from Granka et al. (2004) (0.8842)
and from Joachims et al. (2005) (0.9232). However, it
is still a strong positive correlation, and hence it still
corroborates the evidence that the search result se-
lection behavior is influenced by ranking, with users
showing a tendency to select higher ranked results.
As a possible reason for the relatively lower cor-
relation value, it can be noted that users, on average,
selected lower ranked search results when compared
to related work. In the present study, the selected re-
sult average position was µ = 4.37 with a standard
deviation of σ = 2.87, whereas in the related studies,
users where mostly concentrated in the first two re-
sults. Although this average may appear as an homo-
geneous distribution, approximately 71% of the se-
lections were concentrated in the five higher ranked
results, corroborating that higher ranked results are
selected more often. The selection of lower ranked
results in comparison with related work can be at-
tributed to factors such as:
the participants’ experience with search com-
bined with their education and area of knowledge:
88.89% of the participants declared themselves
very experienced or experienced, hence an “eco-
nomic” profile would be expected, however, their
level of education and their area of knowledge
may lead to other behaviors;
the experiment was carried out in paper, evok-
ing a different behavior from a real search activ-
ity. While people rarely look at everything in Web
pages, not even seeing most of the search results,
with paper sheets they tend to give at least a look
at every result. At the same time, they can’t go
beyond the 10 results presented to them, as they
can on the Web; and
the participants had performed a suggested search
task, and not a natural search activity, which may
have implied in an unnatural search behavior, and
thereafter, an unnatural search result selection be-
havior.
Overall, the evidence acquired through this study
can be useful during informed design decisions for
Exploratory Search tools. Since the observed be-
havior can represent a risk to the success of an Ex-
ploratory Search activity, Exploratory Search tools
should try to encourage users to take a different ap-
proach by exploring more deeply the results. This
could be approached, for example, with novel user in-
terfaces or recommendation systems.
5 CONCLUSION
Although Exploratory Search has been discussed in
several studies in recent years, current search engines
still lack support for this kind of activity. Therefore,
there is still room for models and tools to support the
user in Exploratory Search activities, and the design
of such models and tools can benefit from understand-
ing users’ behavior during this kind of activity. Ac-
cordingly, information about the search result selec-
tion behavior can be particularly useful for informed
design decisions in Exploratory Search tools.
Studies indicate users give more attention and,
therefore, more hits to higher ranked search re-
sults. However, such studies do not allow to con-
clude whether this behavior is only due to the rank-
ing schema, whereas there are other factors possi-
bly influencing user behavior. Therefore, we inves-
tigated whether such behavior remains in a medium
which the user’s attention tends to be distributed more
evenly between the presented results and with no ad-
ditional graphical tricks and interaction possibilities.
We defined a standard distribution of access prob-
ability based on a decay curve according to the search
result position. This standard distribution was eval-
uated against two other distributions from the litera-
ture to be used as a baseline for the experiment. The
sample distribution was then compared to the base-
line and, by means of a strong positive correlation, we
provided evidence the users’ behavior on search result
selection is indeed influenced by the ranking schema.
Such finding is complementary to the aforemen-
tioned related work, with the addition that the influ-
ence occurred even though the results were randomly
reordered, and participants tended to distribute their
attention more equally between the results. Since giv-
ing more attention and, therefore, more hits to higher
ranked results is not ideal for Exploratory Search ac-
tivities, tools for these kind of activities should en-
courage users to explore more deeply the results.
Thereby, the listing presentation may not be the best
choice for the design of Exploratory Search tools,
once it is not guaranteed the most relevant results will
always be high ranked.
For future work we aim to (i) replicate the exper-
iment aiming to achieve a more distributed sample;
(ii) carry out a new experiment containing varied in-
formation needs and, consequently, different results
lists; (iii) conduct another experiment allowing the
participants to select as many results as they want; and
(iv) carry out both qualitative and quantitative stud-
ies to analyze different behaviors of users engaged in
Exploratory Search activities from users engaged in
simpler search activities.
Ordering Matters: An Experimental Study of Ranking Influence on Results Selection Behavior during Exploratory Search
433
ACKNOWLEDGEMENTS
This study was financially supported by Coorde-
nao de Aperfeioamento de Pessoal de Nvel Supe-
rior (CAPES) and Conselho Nacional de Desenvolvi-
mento Cientfico e Tecnolgico (CNPq). The authors
would like to dedicate this work to Professor Srgio
Roberto Pereira da Silva who is dearly missed.
REFERENCES
Aula, A., Majaranta, P., and R¨aih¨a, K.-J. (2005). Eye-
tracking reveals the personal styles for search result
evaluation. In Proc. Int. Conf. IFIP TC13 on Human-
Computer Interaction, pages 1058–1061. Springer-
Verlag.
Basili, V., Caldiera, G., and Rombach, H. D. (1994). Ency-
clopedia of Software Engineering, chapter The Goal
Question Metric Approach, pages 528–532. John Wi-
ley & Sons, Inc.
Bates, M. J. (1989). The design of browsing and berrypick-
ing techniques for the online search interface. Online
Information Review, 13(5):407–424.
Blandford, A. and Attfield, S. (2010). Interacting with In-
formation, volume 3 of Synthesis Lectures on Human-
Centered Informatics. Morgan & Claypool Publishers.
Eisenberg, M. and Barry, C. (1988). Order effects: A study
of the possible influence of presentation order on user
judgments of document relevance. Journal of the
American Society for Information Science, 39(5):293
300.
Granka, L. A., Joachims, T., and Gay, G. (2004). Eye-
tracking analysis of user behavior in www search. In
Proc. Int. Conf. ACM SIGIR on Research and De-
velopment in Information Retrieval, pages 478–479.
ACM.
Hearst, M. (2009). Search User Interfaces. Search User
Interfaces. Cambridge University Press.
Jansen, B. J., Zhang, L., and Mattila, A. S. (2012). User
reactions to search engines logos: investigating brand
knowledge of web search engines. Electronic Com-
merce Research, 12(4):429–454.
Jansen, B. J., Zhang, M., and Schultz, C. D. (2009). Brand
and its effect on user perception of search engine per-
formance. Journal of the American Society for Infor-
mation Science and Technology, 60(8):1572–1595.
Joachims, T., Granka, L., Pan, B., Hembrooke, H., and
Gay, G. (2005). Accurately interpreting clickthrough
data as implicit feedback. In Proc. Int. Conf. ACM
SIGIR on Research and Development in Information
Retrieval, pages 154–161. ACM.
Kules, B. and Capra, R. (2009). Designing exploratory
search tasks for user studies of information seeking
support systems. In Proc. ACM/IEEE-CS Joint Conf.
on Digital libraries, pages 419–420.
Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G.,
and Granka, L. (2007). In google we trust: Users de-
cisions on rank, position, and relevance. Journal of
Computer-Mediated Communication, 12(3):801–823.
Russell, D. and Grimes, C. (2007). Assigned tasks are not
the same as self-chosen web search tasks. In Proc. Int.
Conf. HICSS on System Sciences, pages 83–83.
Van Solingen, R. and Berghout, E. (1999). The
Goal/Question/Metric Method: A Practical Guide
for Quality Improvement of Software Development.
McGraw-Hill Higher Education.
Weinschenk, S. (2011). 100 Things Every Designer Needs
to Know About People. New Riders Publishing.
White, R. W., Capra, R., Golovchinsky, G., Kules, B.,
Smith, C., and Tunkelang, D. (2013). Introduction to
special issue on humancomputer information retrieval.
Information Processing & Management, 49(5):1053 –
1057.
White, R. W., Kules, B., and Bederson, B. (2005). Ex-
ploratory search interfaces: categorization, cluster-
ing and beyond: report on the XSI workshop at the
human-computer interaction laboratory, university of
maryland. SIGIR Forum, 39(2):52–56.
White, R. W. and Roth, R. A. (2009). Exploratory
Search: Beyond the Query-Response Paradigm, vol-
ume 1 of Synthesis Lectures on Information Concepts,
Retrieval, and Services. Morgan & Claypool Publish-
ers.
Wildemuth, B. M. and Freund, L. (2012). Assigning search
tasks designed to elicit exploratory search behaviors.
In Proc. Symp. on Human-Computer Interaction and
Information Retrieval, pages 4:1–4:10. ACM.
Wohlin, C., H¨ost, M., Runeson, P., Ohlsson, M., Regnell,
B., and Wessl´en, A. (2012). Experimentation in Soft-
ware Engineering. Computer Science. Springer.
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