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Contents lists available at ScienceDirect
International Journal of Information Management
journal homepage: www.elsevier.com/locate/ijinfomgt
Web-based interaction: A review of three important human factors
Sherry Y. Chen a,b,∗ , Robert Macredie a
a
b
School of Information Systems, Computing, and Mathematics, Brunel University, Uxbridge, United Kingdom
Graduate Institute of Network Learning Technology, National Central University, Taiwan
a r t i c l e
i n f o
Article history:
Received 4 August 2009
Received in revised form 19 February 2010
Accepted 23 February 2010
Available online xxx
Keywords:
Web-based applications
Prior knowledge
Gender differences, Cognitive styles
a b s t r a c t
With the rapid development of information technology, the World Wide Web has been widely used in
various applications, such as search engines, online learning and electronic commerce. These applications
are used by a diverse population of users with heterogeneous backgrounds, in terms of their knowledge,
skills, and needs. Therefore, human factors are key issues for the development of Web-based applications,
leading research into human factors to grow significantly in the past decade. This paper identifies and
reviews three important human factors that have been examined in existing empirical studies, including
gender differences, prior knowledge, and cognitive styles. The main results from the analysis include
that: (a) females have more disorientation problems than males; (b) flexible paths are more beneficial to
experts while structured content is more useful to novices; and (c) Field Dependent and Field Independent
users prefer to employ different search strategies. In addition to reviewing the existing empirical studies,
this paper also highlights areas of future research.
© 2010 Elsevier Ltd. All rights reserved.
1. Introduction
The World Wide Web (Web) has grown as a unique space
(Serrano, Maguitman, Boguñá, Fortunato, & Vespignani, 2007) and
has become one of our major channels of information and communication (Weinreich, Obendorf, Herder, & Mayer, 2008). The
Web provides a wealth of information to an incredibly diverse user
population (Shneiderman, 2000) and designers face the constant
challenge of developing Web-based applications that need to meet
diverse user needs. Therefore, human factors are increasingly seen
as important issues, as reflected in the substantial number of existing studies in the area. Among various human factors, previous
research has shown that gender differences (e.g., Roy, Taylor, &
Chi, 2003), prior knowledge (e.g., Calisir & Gurel, 2003), and cognitive styles (e.g., Chen & Macredie, 2004) have significant impacts
on Web-based interaction. Furthermore, these three human factors
have certain inter-relations. For example, females tend to behave
similarly to novices, in terms of the extent to which they experience
disorientation problems; males and experts seem to have similar
preferences in their interaction patterns, with studies reporting
that they enjoy non-linear interaction (Ford & Chen, 2000). Despite
the growing number of studies looking at these three human factors, there is a lack of an integrated review which synthesizes their
effects. To this end, this paper will present a state-of-the art review
∗ Corresponding author at: Graduate Institute of Network Learning Technology,
National Central University, Taiwan, ROC. Tel.: +886 3 4227151x35413.
E-mail address: [email protected] (S.Y. Chen).
of the current research associated with these human factors. We
believe that this review will be important for practitioners who
want to develop a sound understanding of the needs and preferences of users with various characteristics, as well as academics
who want to identify topic areas that have been researched or
where research is lacking. This review will also help designers to
develop Web-based personalized applications that can accommodate user’s individual differences. The ultimate goal is that these
Web-based applications can facilitate user’s information seeking,
which, in turn, can improve their performance and perception in
relation to the Web-based applications being used.
The remainder of this paper is organized as follows. Section
2 describes the methods used for this review. The results of
the empirical studies for the three human factors identified are
presented in the subsequent three sections: Section 3 (gender differences); Section 4 (prior knowledge); and Section 5 (cognitive
styles). In addition to the synthesis of empirical findings related
to the three human factors, this review will also discuss the limitations of existing research studies. As such, Section 6 highlights
future research themes by identifying issues that had so far been
paid less attention by scholars in this area. The paper ends with
conclusions drawn from the analysis (Section 7).
2. Review methods
Fig. 1 illustrates the review process, which consists of four
stages. As shown in this figure, Stage 1 was to search the literature within electronic journals (including ACM Digital Library,
IEEE Explore, and ScienceDirect) and using search engines on the
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doi:10.1016/j.ijinfomgt.2010.02.009
Please cite this article in press as: Chen, S. Y., & Macredie, R. Web-based interaction: A review of three important human factors. International
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Table 1
The criteria used to assess quality in stage 4.
Rigor
The research design was appropriate to address the aims of the study.
There was an adequate description of the sample used and the methods
for identifying and recruiting the sample.
Appropriate data collection methods were used and described.
There was adequate justification of the methods applied for data analyses.
Credibility
Sufficient data was presented to support the findings.
There was adequate discussion of the evidence, both for and against the
researcher’s arguments.
Limitations of the study were discussed explicitly.
The conclusions were properly supported/justified by the results.
Fig. 1. The review process for this study.
Internet (including AltaVista, Excite, and Google). These electronic
resources were selected because they were known to include
empirical studies related to gender differences, prior knowledge
and cognitive styles. The search terms for these electronic resources
included four groups: (1) Internet, Web, World Wide Web; (2)
gender, females/males; boys/girls, and men/women; (3) prior
knowledge, system experience, novices/experts, domain expertise,
domain knowledge, computer experience, previous experience,
Internet experience; and (4) cognitive styles, learning styles, Field
Dependence. The first group was combined with each of the other
three groups by using the Boolean ‘AND’ operator. This search strategy resulted in a total of 214 items being identified.
Four main issues were taken into account in selecting studies:
timeline; format; relevance; and quality. Stage 2s purpose was to
select studies based on timeline and format. Regarding timeline,
the review covers studies from 1993 to 2009. 1993 was chosen
as a starting date for the review because the first popular Web
browser, NCSA Mosaic, was introduced in 1993. We argue, therefore, that this year may be considered as the starting point of
Web-based interaction. Regarding format, the aim was to provide
a comprehensive review; thus, not only were studies published
in journals considered, but also those from conference proceedings. However, master’s theses, doctoral dissertations, textbooks,
and unpublished working papers were excluded because it was felt
that academics and practitioners use journals and conference proceeding most often when seeking to acquire information and to
disseminate findings. Also excluded from the review were editorials, prefaces, interviews, news items, reviews, correspondence,
discussions, comments and reader’s letters. Applying these criteria
meant that 78 of the 214 items were excluded.
Stage 3 identified whether the remaining 136 studies were
relevant based on their titles, abstracts and keywords: More specifically, studies were excluded if they did not present relevant
empirical findings or if they were not conducted using Web-based
applications. However, we found that abstracts were of variable
quality, making judgments more difficult to make. Some abstracts
were misleading or unclear, and several gave little indication of
what was in the full article. In particular, it was not always obvious whether their findings are related to the aforementioned three
human factors. Therefore, at this stage, we included all studies
of which findings might reveal relationships between these three
human factors and Web-based applications. If it was unclear from
the title, abstract, and keywords: whether a study conformed to
the screening criteria, it was included so that it could be subject
to a detailed quality assessment in stage 4. As a result of stage 3,
another 61 articles were excluded at this stage, leaving 75 studies
to be considered in stage 4.
Stage 4 assessed the quality of each of the remaining studies by examining the full text of the paper. The quality was
mainly assessed using two criteria: rigor and credibility, which are
described in Table 1. As a result of this analysis, 60 studies were
finally identified and considered in the review.
3. Gender differences
Literature in the computing field has examined gender differences since the early 1980s (Young, 2000) and has recognized
gender as an important variable that influences computing skills.
In general, literature has suggested that males report lower levels
of computer anxiety than their female counterparts; in addition, it
also seems that males achieve much better outcomes than females
in the use of computers (Karavidas, Lim, & Katsikas, 2004). As
the Web has become a popular platform for various applications,
such as search engines, online learning and electronic commerce,
a growing body of studies has been conducted to examine gender
differences in the use of the Web specifically and this literature suggests that major differences between males and females lie within
navigation patterns, attitudes and perceptions.
3.1. Navigation patterns
Navigation is an important issue in Web-based interaction and a
number of studies have found that there are relationships between
gender differences and navigation patterns. Large et al. (2002)
examined how boys and girls behaved differently when retrieving
information from the Web. 53 students, comprising 23 boys and 30
girls from two grade-six classes, were the subjects of their study.
The results revealed that the boys were more actively engaged in
browsing than the girls. Overall, the boys explored more hypertext links per minute, tended to perform more page jumps per
minute, entered more searches in search engines, and gathered
and saved information more often than the girls, while the boys
spent less time viewing pages than the girls. These findings are
similar to those of Roy et al. (2003), who examined student’s navigation styles. 14 eighth-grade students, with an equal number of
boys and girls, participated in their study. Roy et al. (2003) findings
indicated that the boys tended to perform more page jumps per
minute, which indicates that boys navigate the information space
in a non-linear way. Conversely, the girls had a tendency to browse
entire linked documents and to follow a linear navigation approach.
Similar findings are also presented in a recent study by Liu and
Huang (2008), who explored gender differences in an online reading environment by distributing 240 copies of a questionnaire. A
total of 203 completed questionnaires were returned: 80 copies
from males and 123 from females. Their findings showed that male
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readers preferred non-linear reading than female readers. However, Lorigo et al. (2006) obtained different findings in their study
of 36 undergraduate students. They used eye tracking to analyze
how boys and girls searched information when using Google and
their results showed that girls more often returned to previously
visited abstracts while boy’s navigation paths were more likely to
be strictly linear.
Non-linear navigation may cause disorientation problems. Ford
and Miller (1996) studied the influence of gender differences in
information seeking on the Web. 75 postgraduate students (40
males and 35 females) participated in the study. Compared to the
males, the females seemed relatively disoriented and they generally felt themselves unable to find their way around effectively.
Furthermore, Ford, Miller, and Moss (2001) investigated individual differences in Internet searching using a sample of 64 Master’s
students with 20 males and 44 females. In line with their previous
findings, they found that females had difficulties in finding their
way effectively around the Internet, and they were more likely
to get lost and did not feel in control. Another study by Leong
and Hawamdeh (1999) examined gender differences in attitudes to
using Web-based science lessons. 40 primary fifth-grade pupils (17
boys and 23 girls) participated in their study. Leong and Hawamdeh
(1999) reported that the main dislike raised by girls was that they
got lost and did not know what to do next.
The aforementioned studies suggest that females and males
show different approaches to navigation, reflected in the navigation
patterns that they exhibit, but that there are contradictory findings. For example, a study by Hupfer and Detlor (2006) explored
gender differences in information seeking behavior using a selfconcept orientation framework. They received 422 online surveys,
of which 379 were complete, and their analysis of the responses
found no relationships between gender differences and search
frequency.
3.2. Attitudes and perceptions
There has been a great deal of research on the influences of
gender differences on user’s attitudes toward Web-based interaction. However, such research has yielded mixed results. A
number of studies suggest that there are gender differences
in attitudes towards Web-based interaction. An early study by
Jackson, Ervin, Gardner, and Schmitt (2001) examined gender
differences in Internet use. 630 Anglo-American undergraduates
completed the Student Computer and Internet Survey, the results
of which indicated that females reported more computer anxiety and less computer self-efficacy than males. Another early
study by Schumacher and Morahan-Martin (2001) conducted a
survey to identify gender differences in attitudes towards computers and the Internet. The survey was completed by 619 students.
Schumacher and Morahan-Martin’s (2001) findings showed that
females had lower levels of competence and reported feeling higher
levels of discomfort, which, in turn, hampered their computer and
Internet experience. The other study conducted by Liaw (2002)
examined gender differences in attitudes towards using computers and the Web. 809 participants were chosen and asked to record
their perceptions using a Web-based survey over the Internet.
260 respondent’s data were statistically analyzed with the results
showing that male students had more positive attitudes towards
computers and the Web when compared to female students. Similar results were also found in the study by Koohang (2004), which
investigated student’s perceptions towards the use of a digital
library by using a Likert-type instrument. The participants were 154
students enrolled in an undergraduate management program, and
the results indicated that males had significantly higher positive
perceptions than the females toward using the digital library.
3
The studies reviewed so far in this section indicate that females
tend to have more negative attitudes towards the use of the
Web than males and that they feel less able when using the
Web than their male peers. However, other studies indicate that
females show more positive attitudes than males or that there
are no significant differences between females and males. For
example, Kim, Lehto, and Morrison (2007) examined gender differences in online information attitudes, preferences and behaviors
within the domain of travel-related information. 1334 responses
were received from their study, with the results showing that
females demonstrated stronger positive attitudes than males. Furthermore, Koohang and Durante (2003) conducted a study to
measure learner’s perception of a Web-based distance learning
program. 106 undergraduate students (50 males and 56 females)
participated in the study. Gender was one of the variables investigated in the study to determine if differences in perception
towards the Web-based instruction program occurred between
males and females. They found that there was no significant
gender difference towards the Web-based instruction program
in their study. Furthermore, Hong (2002) investigated the relationships between a number of student’s individual differences
(prior computer experience, gender, age, scholastic aptitude and
learning styles) and their perceptions of a Web-based course. The
subjects consisted of 26 students and the results demonstrated
that gender was not significantly related to student’s perceptions. A similar result was found in an earlier study by Ory,
Bullock, and Burnaska (1997), who examined male and female
student use of, and attitudes about, asynchronous learning networks. The study was conducted with 2151 students and the
authors also reported no significant differences between males and
females.
Although some studies show that there is no gender difference
in Web-based interaction, the majority of studies indicate that gender is an influential variable. This implies that males and females
might need different levels of support when they interact with the
Web. As indicated by Ford et al. (2001), gender is a relatively fixed
variable, thus it requires adaptability from a system perspective,
suggesting that it is important that Web-based applications should
be developed to support adaptation to gender.
4. Prior knowledge
User’s prior knowledge includes system experience and domain
knowledge. The former refers to user’s knowledge of the system
being used whereas the latter refers to user’s understanding of
the content area (Lazonder, 2000). Regarding system experience,
Shih, Muñoz, and Sánchez (2006) analyzed the influence of previous system experience on student’s performance in a Web-based
course. 120 students participated in the Web-based course, and
the authors found that the participants with more experience in
the use of Internet tools used less time to organize their work
and visited fewer pages in each session. Regarding domain knowledge, White, Dumais, and Teevan (2008) compared the search
strategies of domain experts with those of domain non-experts
through analysis of over 500 thousand interaction logs. The results
showed that experts issued longer queries than non-experts and
experts also used many more technical query terms than nonexperts.
Regardless of system experience or domain knowledge, a growing body of research has found that low prior knowledge users and
high prior knowledge users show different levels of familiarity and
have different requirements, which are summarized in Table 2.
Such differences are particularly obvious in Web-based instruction and Internet searching. The subsections below discuss their
details.
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Table 2
Low prior knowledge vs. high prior knowledge (adapted from 7).
Familiarity
Requirements
Low prior knowledge
Applying little specialised training
to use the system. They use the
interface that supports the
primary functions.
Such users need an orderly structure,
visible landmarks, reversibility, and
safety during the processes of
interacting with computer systems.
High prior knowledge
Possessing the capability to use
most of the system’s features.
They can get the point quickly
and in a straight way.
Such users demand shortcuts or
macros to speed-repeated tasks and
extensive services to satisfy their
varied needs.
4.1. Web-based instruction
Some research has suggested that individuals with different levels of prior knowledge show preferences for different types of text
structure (Calisir & Gurel, 2003) and different kinds of navigation
facilities (McDonald & Stevenson, 1998b).
4.1.1. Text structure
A number of studies have examined how text structure interacts with user’s prior knowledge; the findings suggest that experts
and novices differ in their performance depending on the text
structure used in Web-based instruction. For example, McDonald
and Stevenson (1998a) examined the effects of text structure and
prior knowledge on navigation performance. Three types of text
structure – hierarchical, non-linear, and mixed (hierarchical structure with cross referential links) – were investigated using 30
university students as the sample. The results showed that the
performance of knowledgeable participants was better than that
of non-knowledgeable participants, as they had a better conception of the subject matter than non-knowledgeable participants.
The results also showed that non-knowledgeable participants performed better in both browsing and navigating in the mixed
structure condition than in the non-linear structure condition.
In a similar vein, Calisir and Gurel (2003) also investigated the
interaction of three types of text structure – linear, hierarchical
and mixed (hierarchical structure with cross referential links) – in
relation to the prior knowledge of users. The same sample size,
30 participants with half being classified as knowledgeable and
half as non-knowledgeable, was used in their study. However, in
contrast to the study by McDonald and Stevenson (1998a), they
examined the influence of text structure and prior knowledge on
learning performance, rather than on navigation performance. The
findings suggest that a hierarchical structure is most appropriate
for non-knowledgeable subjects, probably because this structure
provides a clear insight into the organizational framework of the
subject content. A recent study by Amadieu, Tricot, and MarinéDo
(2005) obtained similar results. They investigated the effects of
prior knowledge on reading and comprehension with 55 psychology students. Three types of structure were provided: hierarchical;
network; and linear. The results indicated that low prior knowledge learners demonstrated better performance in the hierarchical
structure, whereas the hierarchical structure seemed to obstruct
the domain representation for high prior knowledge learners. In
addition to learning performance, text structure also has effects on
learning perception. Mitchell, Chen, and Macredie (2005) examined
how students reacted to Web-based instruction with 74 undergraduate students. They found that students with more system
experience are less likely to struggle with the non-linear interaction whereas those with less system experience showed opposite
responses.
In summary, these findings suggest that experts and novices
differ in their performance depending on the text structure used
and that it is necessary to take user’s prior knowledge into consideration when designing effective text structure for Web-based
instruction. Experts profit most from an instruction system that
provides flexible paths whereas novices seem to benefit more from
a well-structured instruction system. This may be explained by
the fact that experts have acquired a great deal of content knowledge so they are more able to impose structure on the content.
Conversely, novices lack significant domain knowledge; as such,
they prefer text structures that may compensate for their lack of
a conceptual structure of the domain. A hierarchical structure has
been suggested as being most appropriate for novices (DeStefano
& LeFevre, 2007) as it presents a conceptual structure of the material that helps users to identify the relationships among different
topics. By doing so, users can organize their own knowledge and
decide the navigation paths by themselves.
4.1.2. Navigation facilities
Having discussed text structure, it is important to consider how
the text is navigated. Most current Web-based instruction applications provide a range of navigation facilities to allow users to
employ multiple approaches to support their learning. Of these,
hierarchical maps and alphabetical indices are most commonly
used in Web-based instruction; each of them provides different
functions in relation to information access. The characteristics of
the different navigation facilities may influence how users develop
their learning strategies, making navigation support a critical issue.
When considering the relationships between learning strategies
and navigation facilities, student’s prior knowledge is an important factor in determining whether a particular navigation facility
is likely to be useful. In fact, research suggests that a specific navigation facility is unlikely to be useful to a wide variety of users
(e.g., Chen & Macredie, 2004; Ford & Chen, 2000). A number of
empirical studies have found that high and low prior knowledge
users benefit from different types of navigation facilities. Farrell and
Moore (2001) investigated whether the use of different navigation
facilities (linear, main menu and search engine) influence user’s
achievement and attitude. 146 eighth-grade students were placed
into three groups based on their knowledge levels (low, middle,
and high) with the results indicating that high-knowledge users
commonly tended to use search engines to locate specific topics.
Conversely, low-knowledge users seem to benefit from hierarchical
maps, which can facilitate the integration of individual topics.
Furthermore, McDonald and Stevenson (1998b) examined the
effectiveness of navigation facilities and domain expertise in
relation to navigation performance. Three conditions were used
hierarchical map, contents list and hypertext only (no navigation facility). Of the 36 participants, half were classified as
knowledgeable and half as non-knowledgeable based on the
results of individual tests. One of the findings showed that nonknowledgeable subjects performed better in the hierarchical map
condition than in the contents list condition. Similar results were
obtained from another study by Potelle and Rouet (2003), who
investigated the influences of navigation facilities on student’s
comprehension. There were three versions of the system, which
organized information with different navigation facilities: a hierarchical map; a network map; and an alphabetic list. 47 students with
different levels of prior knowledge took part in the experiment and
were assigned to use one of the three versions. The results showed
that the hierarchical map improved comprehension for the lowknowledge participants at the global level. Another recent study by
Minetou, Chen, and Liu (2008) obtained similar results. They conducted two empirical studies to investigate how student’s prior
knowledge affected their use of navigation facilities. 65 learners
participated in Study One and 69 learners in Study Two. The findings of both studies show that hierarchical maps were favored by
users with low prior knowledge. A possible explanation for these
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findings is that the map not only reveals the document structure (i.e.,
the physical arrangement of a document), but also reflects the conceptual structure (i.e., the relationships between different concepts).
In other words, the hierarchical map can help non-knowledgeable
learners to incorporate the document structure into the conceptual structure, so it is useful for them to integrate their knowledge
(Nilsson & Mayer, 2002).
The results of the studies discussed in this section suggest
that students with different levels of prior knowledge benefit
from different navigation facilities. Research suggests that structured navigation facilities, such as hierarchal maps and structural
overviews, are most helpful for novices, as they help them to overcome their lack of conceptual structure of the domain. As indicated
by Nilsson and Mayer (2002), hierarchal maps, which provide learners with structural cues between concepts and as such provide a
global structure, can help learners to integrate their knowledge.
However, such navigational facilities may make the users pay less
attention to the local structure of the content, in turn limiting
their understanding (Hofman & van Oostendorp, 1999). This suggests that there is a need to provide less-knowledgeable users with
navigational facilities that present both global structure and local
structure of the content. The global structure aims to help the users
to find the relevant information and reduce disorientation while the
local structure focuses their attention, with the aim of improving
their deep understanding of a subject.
4.2. Internet searching
In addition to Web-based instruction, several studies also argue
that prior knowledge plays a substantial role in Internet searching,
which covers three aspects: search strategies; search performance;
and search perception. Regarding search strategies, Tabatabai
and Luconi (1998) investigated different strategies used by three
experts and three novices. The results showed that experts used
more keywords while novices used the ‘Back’ key more often;
used fewer search engines; and missed some highly relevant sites.
Another study by Tabatabai and Shore (2005) compared search
strategies used by 10 experts; nine intermediates and 10 novices.
They found that novices used significantly fewer meta-cognitive
strategies than intermediates or experts; and that experts spent
more of their time in using cognitive strategies than the other
two groups of users. The other recent study by Thatcher (2008)
investigated relationships between Web experience and cognitive
search strategies. 80 participants took part in their study; with
the results showing that participants with higher levels of Web
experience were more likely to use ‘parallel player’; ‘parallel huband-spoke’; ‘known address search domain’ and ‘known address’
strategies. Conversely; participants with lower levels of Web experience were more likely to use ‘virtual tourist’; ‘link-dependent’;
‘to-the-point’; ‘sequential player’; ‘search engine narrowing’; and
‘broad first’ strategies.
Regarding search performance, a study by Aula and Nordhausen
(2006) examined how users locate information with search
engines. 22 participants performed three fact-finding tasks and
two broader tasks. The goal of each fact-finding task was to find
one specific answer to a simple question while the broader tasks
required the participants to find several documents that would satisfy the task. The results indicated that no significant differences
were noted between experts and novices regarding the fact-finding
tasks but that experts completed the two broader tasks in significantly less time than novices. Additionally, an earlier study by
Lazonder, Biemans, and Wopereis (2000) compared differences
between novices and experts in searching information on the Web.
25 students were observed while performing search tasks; the
results showed that the experts needed fewer actions and less time
to find relevant Web sites. The other early study by Höscher and
5
Table 3
Field Dependence vs. Field Independence (adapted from Witkin et al., 1977).
Field Dependent users
Field Independent users
They are externally directed
and are easily influenced by
salient features.
They experience surroundings
in a relatively global fashion
and struggle with individual
elements.
They are more likely to accept
ideas as presented.
They are internally directed and
process information with their own
structure.
They experience surroundings
analytically and are good with
problems that require taking elements
out of their whole context.
They are more likely to accept ideas
only strengthened through analysis.
Strube (2000) investigated Web searching of Internet Experts and
‘Newbies’ with 24 participants in the field of economics and found
that people with considerable background knowledge about the
domain spent significantly less time reading a document in that
domain.
Regarding search perception, Ahmed, McKnight, and
Oppenheim (2004) examined how users perceived the Web
of Science interface, through a sample of 22 participants. The
results from the questionnaire showed that the novice group was
significantly more satisfied with the interface than the experienced group. Furthermore, another study by Yang, Tsao, Lay, Chen,
and Liou (2008) used a questionnaire to measure the Internet
self-efficacy, language self-efficacy, foreign language classroom
anxiety, language experience, and intention to use commercial
Web sites of 418 participants. The results indicated that prior
non-native language experience had a positive effect on intention
to use non-native language commercial Web sites. A recent study
by Park, Roman, Lee, and Chung (2009) investigated the factors
that influenced people’s adoption and use of a digital library
system with the Technology Acceptance Model (TAM). Data were
collected from 16 institutions in developing countries and 1082
surveys were analyzed. The results revealed that experience in
computer use and domain knowledge had positive effects on the
perceived ease of use of the digital library system.
The studies presented in this section reinforce the findings that
users with different levels of prior knowledge tend to use different approaches to process information. Such findings should
be incorporated into the development of personalized Web-based
instruction programs and Internet searching environments so that
the needs and preferences of both experts and novices can be met.
5. Cognitive styles
Like prior knowledge, cognitive style also plays an essential role
in Web-based instruction and Internet searching. Within the area
of cognitive styles, Field Dependence has emerged as one of the
most widely studied human factors in relation to these two types
of Web-based application. Field Dependence refers to the degree to
which a user’s perception or comprehension of information is influenced by the surrounding perceptual or contextual field (Jonassen
& Grabowski, 1993). The key issue in Field Dependence lies within
the differences between Field Dependent and Field Independent
users, which are presented in Table 3.
5.1. Web-based instruction
Several studies have investigated the relationships between the
degree of Field Dependence and student’s learning performance
and learning preferences with respect to Web-based instruction.
5.1.1. Learning performance
A number of studies have shown that the learning performance
of Field Independent and Field Dependent students is associated
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with the design of Web-based instruction material. Ford and Chen
(2001), for example, investigated the effects of cognitive style
on learning performance in different versions (breadth-first and
depth-first) of a Web-based instruction system, with student’s
cognitive styles determined using Riding’s Cognitive Style Analysis (CSA, Riding, 1991). 73 students participated in their study.
Ford and Chen (2000) found that Field Dependent learners in the
breadth-first version performed better than those in the depth-first
version. Conversely, Field Independent students performed better
in the depth-first version than those in the breadth-first version.
Another study by Parkinson and Redmond (2002) investigated the
impacts of three treatments – Text, CD-ROM and Internet site –
and cognitive style on learning performance. Their study included
47 participants. Both Group Embedded Figures Test (GEFT, Witkin,
Moore, Goodenough, & Cox, 1977) and the CSA were applied to
identify the participant’s cognitive styles. The results from the
GEFT showed that Field Independent students performed better in
the Internet treatment than in the CD-ROM and Text treatments.
The other study by Graff (2003) used a sample of 50 students
and applied the CSA to determine an individual’s cognitive style,
examining the relationship between cognitive style and performance in two versions of the system – long-page and short-page
versions. The study’s findings indicated that Field Independent students achieved superior scores in the long-page condition whereas
Field Dependent students were superior in the short-page condition. A more recent study by Umar and Maswan (2007) examined
two versions of Web-based instruction – guided inquiry and tutorial. The GEFT was applied to measure the cognitive styles of 141
high-school students, with the study’s results showing that the
Field Independent group in the guided inquiry version performed
significantly better than those in the tutorial group.
The studies mentioned so far in this section suggest a strong
relationship between student’s learning performance and their
cognitive style, but a number of studies have found no differences between Field Independent and Field Dependent individuals
in this context. For example, a study by Shih and Gamon (2002)
analyzed the relationships between student achievement, learning
strategies, learning patterns, learning styles and student characteristics. There were 99 participants, whose cognitive styles were
determined by the GEFT. The study’s results indicated that no significant differences were found for learning achievement between
Field Independent and Field Dependent students. Another study, by
Altun and Cakan (2006), examined the correlations between cognitive styles and academic achievement. In this study, the GEFT
was used to identify the cognitive styles of the participants, which
included 130 teacher trainees, and, like Shih and Gamon (2002),
they found that there was no significant relationship between cognitive styles and academic achievement.
5.1.2. Learning preferences
Learning preferences are the choices that learners show in certain types of learning environments and activities (Lee, McCool,
& Napieralski, 2000), such as the selection of certain navigation
paths or facilities. Several studies provide evidence that Field
Independent and Field Dependent students show different learning preferences. For instance, Lee, Cheng, Rai, and Depickere
(2005) investigated student’s learning preferences in WebCT (a
Web-based instruction environment). Their sample comprised 217
students from Murdoch University and the findings indicated that
non-linear learning was the primary dimension that reflects student’s cognitive styles. Field Dependent students were accustomed
to linear learning whereas Field Independent students tended to
have a preference for non-linear learning. These findings reflect
those of Chen and Macredie (2004), who examined student’s learning preferences by conducting a survey of 61 Master students
whose cognitive styles were determined using Riding’s CSA. The
results showed that Field Independent students appreciated the
fact that Web-based instruction programs allowed them to study
topics in any order, whereas Field Dependent students felt confused
over which options they should choose.
In addition to non-linear learning, Field Independent and Field
Dependent students also demonstrate different learning preferences for content presentation and navigation tools. Regarding
content presentation, Lu, Yu, and Liu (2003) investigated student’s
learning patterns and learning styles in a WebCT course. 76 graduate students participated in their study and the GEFT was used
to classify the participant’s cognitive styles. Their study found that
Field Dependent subjects made more use, more often of the teaching notes and other class resources than Field Independent subjects.
Regarding navigation tools, Ford and Chen (2000) examined the
effects of cognitive styles on student’s selection of navigation tools,
with 65 students taking part in their study. Riding’s CSA was applied
to identify student’s cognitive styles. They found that Field Independent students preferred to use an alphabetical index to locate
specific information while Field Dependent students tended to use
a hierarchical map to get a global picture of the subject content.
Another recent study, by Lin and Chen (2008), showed similar
results. 101 individuals participated in their study and the participant’s cognitive styles were identified by the CSA. The study’s
results showed that Field Independent individuals favored using an
alphabetical index and a search engine whereas Field Dependent
individuals preferred to use a map.
5.2. Internet searching
Previous research also indicates that there are relationships
between Field Dependence and Internet Searching. For example,
Wang, Hawk, and Tenopir (2000) studied user’s interactions with
Web resources, using 24 graduate students as participants. The
GEFT was used to identify the participant’s cognitive styles and participants were asked to find answers from the Web for two search
questions. The results showed that there were a statistically significant correlation between GEFT scores and the time spent for
searching and the URLs visited. The participants with the higher
GEFT scores conducted the longer search sessions, and visited more
URLs. In contrast, the participants with the lower GEFT scores had
the shorter search sessions. A relevant study is that of Palmquist
and Kim (2000), which investigated the effects of cognitive styles
and online database search experience on Web search performance.
The GEFT was administered to identify the cognitive styles of 48
undergraduate college students. The study’s results showed that
Field Dependent novices frequently made greater use of embedded links than other users. Additionally, Kim, Yun, and Kim (2004)
compared search strategies of different cognitive style groups with
50 participants. Their results showed that the Field Dependent
group demonstrated significantly more repeated search attempts
and total search attempts and also made more use of search operators.
Unlike the aforementioned studies which identified the user’s
cognitive styles with the GEFT, other studies selected the CSA to
measure cognitive style. For example, a study by Ford, Miller, and
Moss (2005) sought to discover the effects of human individual differences on Web search strategies. Their sample consisted of 250
Master students and the study’s results indicate that there is a link
between low levels of Boolean searching (where individual search
terms are combined using operators such as AND and OR) and Field
Independence, and a link between high levels of Boolean searching
and Field Dependence. Furthermore, a more recent study by FriasMartinez, Chen, and Liu (2008) examined how users behaved in a
digital library with 50 individuals, and their results showed that
Field Dependent users frequently used ‘back’/‘forward’ buttons,
whereas Field Independent users had a notable use of advanced
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Table 4
Summary of the findings of the empirical studies.
Section
Human factor
Summary
3
Gender differences
4
Prior knowledge
Some studies found that there are no
gender differences in navigation
patterns and attitudes toward
Web-based interaction but the
majority of studies indicated that
females and males showed different
behavior and demonstrated different
perceptions and attitudes. In
particular, females encountered more
disorientation problems and had more
negative attitudes than males.
Regarding Web-based instruction, the
studies suggest that flexible paths are
more beneficial to experts while
structured content is more useful to
novices, who can particularly get more
benefits from a hierarchical map.
Regarding Internet searching, experts
and novices used different types of
search strategies, spent different
amount of time in doing broader tasks
and reading documents and show
different perceptions.
Regarding Web-based instruction, the
studies suggest that Field Dependent
and Field Independent users
demonstrate different learning
preferences but results remain
inconclusive in terms of learning
performance. Regarding Internet
searching„ the studies suggest that
Field Dependent and Field
Independent users prefer to use
different search strategies, especially
in terms of the use of embedded links
and Boolean searching.
5
Cognitive styles
search.
To summarize the studies presented in Section 5, Field Dependent and Field Independent users behave differently in Web-based
instruction and Internet searching. Nevertheless, as the review in
Section 5.1 indicates, evidence of whether Field Dependence has an
effect on learning performance is still inconclusive, suggesting the
need for further research to seek to clarify this issue.
Sections 3–5 have reviewed the empirical studies that examined
how human factors affected user’s interactions with the Web, with
an emphasis on gender differences, prior knowledge and cognitive
styles. Table 4 presents a summary of the findings of these empirical
studies.
6. Limitations and future trends
The studies presented in the last three sections demonstrate that
many researchers have made serious efforts to investigate the relationships between human factors and Web-based interaction. Their
contributions are important but there are still some limitations
with the current research, of which the research and development
communities need to be aware and which should be investigated
through future research. These limitations are discussed below.
6.1. Inconsistent results
As the discussion in Sections 3–5 demonstrates, the results
reported in existing studies are not fully consistent. For example,
there are contradictory findings as to whether gender differences
influence user’s attitudes and perceptions towards Web-based
interaction and whether cognitive styles affect user’s learning performance. Further empirical research is needed to identify more
7
conclusively the effects of these human factors on Web-based interaction and to explain variations in the results from studies in the
area. The results should help to reinforce the findings of some of the
existing studies and better guide the development and evaluation
of Web-based applications.
In addition to conducting further empirical studies to verify the
results, researchers in the field also need to investigate the research
instruments that they use. In the studies reported in this paper,
a wide range of research instruments were employed to gather
evidence on core user ‘characteristics’, leading to problems of comparability of findings when seeking to look across different studies.
In particular, researchers have used different types of questionnaires to identify perceptions and attitudes. In the future, there
is a need to examine the reliability and validity of such questionnaires and it may be useful to develop a standard template for the
questionnaires so that the accuracy of the results can be improved.
6.2. Traditional statistics
Existing studies mainly analyze data with traditional statistical approaches, including parametric and non-parametric analyses.
These approaches aim to indicate trends related to user’s preferences. However, expanding the repertoire of techniques for data
analysis used in this field to include advanced intelligent technologies, such as data mining, will offer researchers different insights
into Web-based interaction. The main difference between traditional statistical approaches and data mining lies in the aim that is
sought: the former is used to verify existing knowledge in order to
prove a known relationship (Moss & Atre, 2003); while the latter
is aimed at finding unexpected relationships (Wang, Ree, & Liao,
2002). As opposed to traditional experiments designed to verify
a priori hypotheses with statistical analyses, data mining uses the
data itself to uncover relationships and patterns. By doing so, hidden relationships and patterns can be discovered and predictive
rules can be generated (Gargano & Ragged, 1999).
Recent studies (such as those of Chen & Liu, 2008; Cho, Kim, &
Kim, 2003) suggest that data mining is a useful tool for analyzing
Web usage data because it can discover regularities and patterns
hidden in data. Among these studies, Chen and Liu (2008) particularly have highlighted that data mining can discover some findings
that cannot be found using traditional statistical analysis. More
specifically, data mining offers future research the potential to analyze how human factors influence Web-based interaction and we
would suggest that this is embraced by the research community to
add a new methodological dimension to their studies.
6.3. Lack of mixed methods
The literature suggests that quantitative methods are favored
when seeking to find the overall effectiveness of the systems being
studies (Legree, Gillis, & Orey, 1993) whereas qualitative methods
are more often used to provide information in relation to personal interaction and perception (Murray, 1993). It is clear that
quantitative and qualitative methods have different strengths and
weaknesses. As such, a research design should combine instances
of both types of method in order to overcome the individual weaknesses and biases of each method (Rossman & Wilson, 1985).
However, existing studies mainly use quantitative methods and
a paucity of research uses qualitative methods or mixes quantitative and qualitative methods. The isolated use of the quantitative
methods may be artificial and shallow and lead to the loss of large
and valuable sources of information (Kaplan & Maxwell, 1994).
Methods should be used in a complementary way; as implied
by Isaacs and Tang (1994, p. 27), objective quantitative results,
such as frequency counts, can identify reliable patterns in the data
which confirm or deny theories about user behavior whereas sub-
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jective qualitative data informs as to how and why the behavior
takes shape. Future research in the area should consider combining quantitative and qualitative methods into coherent and
balanced research designs which can, in turn, offer a more complete understanding of the relationships between human factors
and Web-based interaction.
Acknowledgement
6.4. Limited applications
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distance learning activities/assignments portion of an undergraduate hybrid
As indicated in Section 1, the Web includes a wealth of information and has been used to deliver a variety of applications,
including online learning, search engines, digital libraries, Internet
banking, and electronic shopping. However, the majority of existing empirical studies focus on Web-based instruction or Internet
searching, leaving other types of Web-based applications relatively
less explored and understood.
Among these applications, online shopping is becoming increasingly popular with consumers. In traditional shopping, the
interaction between consumers and the physical shop determines
the impression that the consumers have of the shop. However, in
online shopping the Web site may be thought of as a ‘window’
through which users have their initial interaction with the shop
(Zhang & von Dran, 2002) and the design of which presents the
users with a comprehensive image of the shop (Tung, 2001). Yet,
users may have different preferences for the design of Web sites
and, as such, human factors form important considerations in the
design of Web sites for online shopping.
7. Conclusions
The studies reviewed in this paper suggest that human factors
play an important role in the development of Web-based applications and users with different cognitive styles, levels of prior
knowledge and gender use different approaches in their interactions with the Web. Therefore, these human factors must be taken
into account in the development of Web-based applications. In
other words, different perspectives of Web design should be available for potential matching with various user’s characteristics and
preferences. If Web-based applications can be aware of such differences, they may be able to offer appropriate support to each
individual, possibly resulting in the improvement of user performance and user perception.
In this paper, we have given an overview of the influences of
human factors on Web-based interaction. This overview, which
provides additional insight into information management and
information technology, is useful in the development of Web-based
applications. Interested readers may further investigate the details
through the references listed in this paper. Even where we see
problems or shortcomings in the design of the studies and the applications considered, understanding their scope and limitations can
be very useful in avoiding similar problems, whether in the design
of subsequent studies or of Web-based applications themselves.
Although the focus in this paper has been on three key human
factors – gender differences, prior knowledge and cognitive styles
– it should be noted that other human factors also affect user’s
interaction with the Web; these include affective factors, cultural
background and age differences. An important direction for future
research is the investigation of this wider set of factors and the identification of the major design features which interact with each. This
might lead to the development of concrete criteria for the evaluation of Web-based applications in relation to this expanded set of
human factors issues and guidelines to support the development of
Web-based applications. The eventual aim is to enhance the function and performance of Web-based applications by matching them
to the needs and preferences of each individual.
The work presented in this paper is funded by National Science
Council, Taiwan (NSC 98 - 2511 - S - 008 - 012 - MY3).
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Sherry Y. Chen is currently a Professor at Graduate Institute of Network Learning
Technology, National Central University, Taiwan and a Reader in the Department
of Information Systems and Computing at Brunel University, UK. She obtained her
PhD from Department of Information Studies, University of Sheffield, UK in 2000.
Her current research interests include human–computer interaction, data mining,
digital libraries, and educational technology. She has almost 100 publications in
these areas. She is the Associate Editor of Online Information Review and International Journal of Digital Library Systems. In addition, she acts as a member of the
editorial board of ten computing journals.
Robert D. Macredie is Professor of Interactive Systems in the Department of Information Systems and Computing at Brunel University, UK. He has worked with a
range of organizations, ranging from large, blue-chip companies, through small
businesses, to government agencies. Robert’s key interest lies in the way in which
people and organizations use technology, and his research aims to determine how
work can be more effectively undertaken by improving the way that we understand how people and technology interact in organizational (and social) settings.
He has undertaken work on a range of issues associated with people, technology
and organizations and has over 180 published research contributions in these areas.
Please cite this article in press as: Chen, S. Y., & Macredie, R. Web-based interaction: A review of three important human factors. International
Journal of Information Management (2010), doi:10.1016/j.ijinfomgt.2010.02.009