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MEASURING PERCEIVED WEBSITE USABILITY
Jianfeng Wang & Sylvain Senecal
ABSTRACT. The objective of this research was to develop a short, reliable, and valid perceived
website usability measurement scale. A sample of 350 participants was used to collect the
necessary data. Exploratory and confirmatory factor analyses were performed to purify the
proposed scale. Analysis indicated that the proposed multi-dimensional usability scale is reliable
and shows evidence of construct and predictive validity. Academic and managerial implications
were discussed.
KEYWORDS. Website usability, ease-of-navigation, speed, interactivity, user attitude
Jianfeng Wang, Ph.D., is an Assistant Professor of Marketing, Department of Business &
Economics, Mansfield University of Pennsylvania, Mansfield, PA 16933 (e-
mail:pwang@mansfield.edu)
Sylvain Senecal, Ph.D., is an Associate Professor of Marketing, Department of Marketing, HEC
Montreal, Montreal (Quebec), Canada H3T 2A7 (e-mail: sylvain.senecal@hec.ca)
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MEASURING PERCEIVED WEBSITE USABILITY
INTRODUCTION
The experience consumers have on a website is increasingly becoming an important topic
both for academia (Agarwal and Karahanna, 2000; Novak, Hoffman & Yung, 2000) and for
organizations using websites to market their products and services. The website design is an
important determinant of visitors’ online purchases and revisit intentions (Hill, 2001; Klein,
1998). Moreover, Nielsen (2000, p.10) argues that “users experience usability of a site before
they have committed to using it and before they have spent any money on potential purchases.”
Thus, developing sites that are easy to use and that meet organizational needs is critical for
organizations. One construct that may be useful in evaluating websites and consequently
developing better websites is usability.
The objective of this research is to develop a short, reliable, and valid perceived usability
measurement scale. The aim is to develop a parsimonious scale that can be used across websites.
Thus, the measurement scale could be used for benchmarking purposes within an organization
and/or across organizations. For instance, an organization could measure consumers’ perception
of its website usability and of their competitors’ websites in order to benchmark their website
with the competition. The development of a usability measurement scale that shows evidence of
reliability and construct validity would also be useful to researchers in order to investigate the
relationship between perceived usability and other relevant constructs such as attitude toward the
website and intention to revisit the website (Cook & Campbell, 1979; Straub, 1989).
THEORETICAL BACKGROUND AND HYPOTHESES
2
Usability and Functionality
The notion of usability is a key theme in the human-computer interaction (HCI) literature.
Research in the HCI tradition has long asserted that the study of human factors is crucial to the
successful design and implementation of technological devices. The overarching goal of a
majority of the HCI work has been to propose techniques, methods, and guidelines for designing
better and more “usable” artifacts. Drawing upon cognitive frameworks of human-computer
interaction grounded in psychology, prior research developed user models that delineated the
cognitive structures driving user behavior (Card, Moran, & Newell, 1983).
The quality of a website can be assessed in different ways. To date, studies of websites
have focused on website functionality and on website usability. A system is said to be functional
when it provides functions needed by users to perform their tasks (Goodwin, 1987). A website
can be evaluated based on the presence or absence of certain functions or on the performance of
those functions. However, past research has found that users’ acceptance of a system is
contingent not only on its functionality but also on its usability (Davis, 1986; Goodwin, 1987).
The concept of usability can be defined as “how well and how easily a user, without
formal training, can interact with an information system of a website” (Benbunan-Fich, 2001).
Bernard et al. (1981) suggested that a “truly usable system must be compatible not only with the
characteristics of human perception and action, but, most critically, with users’ cognitive skills in
communication, understanding, memory, and problem solving.” A usability evaluation
consequently assesses the ease of use of a website functions and how well they enable users to
perform their tasks efficiently. Thus, usability is a more inclusive construct than functionality.
Usability Metrics
3
A variety of alternative approaches to usability evaluation have been proposed in prior
work. Melody et al. (2001) identify five distinct approaches: testing, inspection, inquiry,
analytical modeling, and simulation. Among these approaches, one common characteristic of
usability evaluation methods is their dependence on subjective assessments in the form of user
judgments. Thus, usability is not intrinsically objective in nature, but rather is closely intertwined
with an evaluator’s personal interpretation of the artifact and his or her interaction with it
(Agarwal & Venkatesh, 2002). Although self-reported measures are commonly used, research
shows that perceived ease of use of a system is strongly correlated to subjective system usage
measures, but weakly correlated to objective system usage measures (Straub, Limayem, and
Karahanna-Evaristo 1995; Barnett at al. 2006).
Research has been ongoing in identifying approaches to improve online usability (Boling,
1995; Levi & Conrad, 1996; Nantel & Senecal, 2007; Palmer, 2002; Pitkow & Kehoe, 1996).
Studies often focus on the download delay, success in finding a page or completing a task, or
organization of the information gathered during a Web session (Pitkow & Kehoe, 1996; Nantel
& Senecal, 2007). For instance, Nantel and Senecal (2007) suggest that there is a positive
relationship between the time users spend waiting for webpages to download and the probability
that they will complete their task on the website. Other research is based on Microsoft Usability
Guidelines (MUG). Five major categories are proposed as relevant while designing websites for
business: content (relevance, media use, depth/breadth, current information), ease of use (goals,
structure, feedback), promotion, made-for-the medium (community, personalization, refinement),
and emotion (challenge, plot, character strength, pace) (Agarwal & Venkatesh, 2002; Venkatesh
& Ramesh, 2006; Venkatesh & Agarwal, 2006).
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To date, the literature has conceptualized usability as either a unidimensional construct or
a multidimensional construct composed of two dimensions (Table 1). Except for Palmer (2002),
most research has not explored usability as a construct composed of more than two dimensions.
Based on the current literature, we suggest that usability is composed of at least three dimensions:
ease-of-use navigation, speed, and interactivity. Table 1 provides a summary of the research on
the three main dimensions used to assess the usability construct.
TABLE 1. Usability Metrics Used in Prior Research
Ease-of-
Navigation
Speed Interactivity
Agarwal & Venkatesh (2002) Y Y
Barnes & Vidgen (2001) Y Y
Lewis (1995) Y
Loiacono, Watson, & Goodhue (2002) Y Y
Nielsen (1999) Y
Palmer (2002) Y Y Y
Raquel (2001) Y Y
Tilson, Dong, Martin, & Kiele (1998) Y Y
Venkatesh & Ramesh (2006) Y Y
Venkatesh & Agarwal (2006) Y Y
Of the various factors that contribute to usability of a website, ease of navigation has
been deemed important by a majority of researchers (See Table 1). Ease of navigation relates to
the level of time and effort required to accomplish specific tasks (Venkatesh, 2000). Good
navigation design helps users acquire more of the information they are seeking and makes the
information easier to find. Thus, a key challenge in building a usable website is to develop a
good navigational structure and appropriate hyperlinks. Ease-of-navigation is analogous in
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essence to the ease of use in IT research (Davis, Bagozzi, & Warshaw, 1989), but it is specific to
website navigation.
According to several authors download delay is also an important design criterion on the
Internet (See Table 1). Speed is important since it enables users to attain their goals without too
much wait. Dalleart and Kahn (1999) argued that consumers were able to separate the evaluation
of waiting experiences from the evaluation of the website. However, they also show that when
there is uncertainty about the waiting (as with the majority of downloads), the negative feeling
generated by the waiting experience carries over to the evaluation of the website. They suggest
that waiting for the homepage to download was less damaging to the website evaluation than
having to wait during the interaction with the website. Their study revealed that delays shorter
than expected led to better evaluations of the website. In addition, Bucklin and Sismeiro (2003)
suggest that there is a negative relationship between downloading time for a web page and the
probability of requesting an additional web page within a website. It has to be noted that since
the focus of this study is on elements that a website can control, the objective measure of
download delay will not be taken into account; only user perception will be assessed.
When users choose to use a technology, they are also choosing to interact with that
technology (Orlikowski, 2000). A key capability of the Internet is its capacity to support greater
interaction for users (Palmer, 2002). Interactivity can be defined as a characteristic of a
computer-mediated communication in the marketplace that increases with the bidirectionality,
timeliness, mutual controllability, and responsiveness of communication as perceived by
consumers and firms (Yadav & Varadarajan 2005). For instance, interactivity can be used to
make the website personalizable. Venkatesh & Ramesh (2006) argue that the ability to customize
websites is an important design characteristic because it helps users save time and provides
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information that is of greatest interest to them. Thus, as suggested by several authors (See Table
1), we suggest that website interactivity is also an underlying dimension of website usability.
Research Model and Hypotheses
Our conceptual framework is presented in Figure 1. Based on the literature review, we
propose that ease-of-navigation, speed, and interactivity are three underlying dimensions of the
usability construct. They are first-order factors that share a common variance that reflects a
single second-order factor labeled as website usability. Thus, the following hypothesis is posited.
H1: Website usability is a single second-order factor with three first-order factors,
namely ease-of-navigation, speed, and interactivity.
H1a: Ease-of-navigation is an underlying factor of Website usability.
H1b: Speed is an underlying factor of Website usability.
H1c: Interactivity is an underlying factor of Website usability.
FIGURE 1. Conceptual Framework
As suggested in the HCI literature, technological artifacts that are more usable are likely
to change user’s cognitions, thus engender positive attitudes. Usable systems not only meet the
instrumental goals of users, but also alleviate the cognitive effort associated with use (Nielsen,
Website
Usability
Attitude
toward the
Website
Ease-of-Navigation
Speed
Interactivity
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2000). Based on empirical results from the Technology Acceptance Model (Davis, 1989; Davis,
Bagozzi, & Warshaw, 1989), a usable website should generate a positive attitude toward it (See
Figure 1). Thus, a positive correlation should exist between the usability construct and attitude
toward the website. A positive relationship would provide evidence of predictive validity of our
measurement scale.
H2: There is a positive correlation between consumers’ perceived website usability and
their attitude toward the website.
METHODOLOGY
Data
The sample was composed of three hundred and fifty undergraduate business students. Each
student was asked to go to a specific transactional website (www.eddiebauer.com) and perform
the following simple tasks: 1) Find and read about Eddie Bauer’s Children Privacy Policy; 2)
Search and select a sweater that he/she would like to buy, add it to shopping cart, but do not
checkout; 3) Find the Eddie Bauer 3-in-1 car seat for the children, add to the shopping cart, but
do not checkout; and 4) Remove the sweater and the car seat from the shopping cart, and exit the
website. Then, they were asked to complete a paper-pencil questionnaire. The questionnaire was
used to assess their perception of the website’s usability and also their attitude toward the
website.
Measurement Scales
Items adapted from previous research on website ease-of-navigation (Loiacono, Watson,
& Goodhue, 2002; Lewis 1995; Nielsen, 1999), speed (Nielsen 1999; Palmer 2002; Loiacono,
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Watson, & Goodhue, 2002), and interactivity (Agarwal & Venkatesh, 2002; Palmer 2002; Tilson,
Dong, Martin, & Kiele, 1998; Barnes & Vidgen, 2001) were used to assess each dimension of
website usability. The following criteria were used to select scale items: (1) items had to focus
on a single dimension, not bridge two or more dimensions, a feature critical for discriminant
validity, (2) they had to use, or be adaptable to, a common format for ease of administration (i.e.,
a seven-point Likert scale). The items used for each dimensions are presented in the Appendix.
An adapted version of attitude toward the website scale (Chen & Wells, 1999) was used to assess
participants’ attitude toward the website (See Appendix for specific items).
RESULTS
The objective of the analysis was to examine the measurement scale reliability and initial
construct validity of the three-dimensional website usability measurement scale. First,
descriptive statistics and initial reliability estimates were computed. Second, an exploratory
factor analysis was performed to test the proposed structure of the measurement scale and to
purify the scale by eliminating items if necessary. Third, a confirmatory factor analysis was
performed with the remaining items to verify the scale and test that usability is a second-order
construct with a more robust test. Finally, a regression analysis was performed to test the
relationship between perceived usability and attitude toward the website.
Descriptive Statistics and Reliability Estimates
Table 2 gives univariate statistics and correlations among the website usability items. In
general, participants reported a fairly strong sense of website usability. More importantly, in
general correlations between items from the same dimension (See triangles in Table 2) were
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higher than correlations between items from two different dimensions. The only exception was
item IRC4 which showed higher correlation with items not in the interactivity dimension. The
Cronbach’s alpha coefficients are 0.88, 0.94, and 0.80 for navigation, speed and interactivity
respectively. Meanwhile, the Cronbach’s alpha coefficient for attitude measurement scale was
0.77.
TABLE 2. Univariate Statistics and Pearson Correlations among Usability Items*
Items** Mean*** S.D. 1 2 3 4 5 6 7 8 9 10 11
1. NAV1 5.57 1.15
2. NAV2 5.63 1.11 0.68
3. NAV3 5.34 1.24 0.67 0.69
4. NAV4 5.68 1.13 0.63 0.67 0.60
5. S1 5.67 1.29 0.50 0.49 0.44 0.45
6. S2 5.54 1.20 0.47 0.56 0.48 0.48 0.79
7. S3 5.51 1.33 0.46 0.50 0.46 0.44 0.77 0.76
8. S4 5.37 1.27 0.48 0.48 0.39 0.48 0.78 0.78 0.83
9. IRC1 4.97 1.30 0.35 0.5 0.45 0.36 0.43 0.47 0.40 0.39
10. IRC2 4.72 1.27 0.26 0.35 0.40 0.23 0.26 0.32 0.30 0.29 0.46
11. IRC3 4.95 1.19 0.26 0.30 0.37 0.23 0.26 0.31 0.32 0.30 0.50 0.63
12. IRC4 5.25 1.25 0.51 0.58 0.54 0.52 0.49 0.54 0.55 0.56 0.43 0.48 0.45
* Significance: p < 0.001.
** See the Appendix for items
***All items measured with a 7-point Likert type scale
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Exploratory Factor Analysis
Items were then submitted to an exploratory factor analysis using principal components
as means of extraction and varimax as method of rotation. Three factors, displayed in Table 3,
emerged. Speed explained 53% of the variance, Ease-of-Navigation 12%, and Interactivity 10%.
As recommended for exploratory work by Nunnally (1967), only loadings above 0.60 are
displayed. As suspected following the correlation analysis (Table 2), IRC4 did not meet the 0.60
threshold and was removed. All three factors had eigenvalues greater than one and individually
explained a significant portion of the usability variance.
TABLE 3. Exploratory Factor Analysis Results
Items (Abbreviated) Factor Loadings
NAV1
NAV2
NAV3
NAV4
S1
S2
S3
S4
IRC1
IRC2
IRC3
IRC4
0.86
0.82
0.86
0.88
0.81
0.80
0.78
0.80
0.62
0.85
0.86
0.54
Factor Labels
Speed
Ease-of-
Navigation
Interactivity
Eigenvalues
Percentage Variance Explained
6.33
52.8
1.46
12.2
1.22
10.2
Hypothesis Testing
A second-order confirmatory factor analysis (CFA) was conducted to assess the
discriminant validity of the usability items and the contribution of the three dimensions to the
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overall construct of usability (Hypothesis 1). We used Structural Equation Modeling (SEM)
(Lisrel 8.72 software) to perform the CFA.
Following Sethi and King (1994), iterative modifications were made for each of the
constructs by observing modification indices and coefficients to improve key model fit statistics.
Further, as recommended by Joreskog and Sorbom (1989), only one item was altered at a time to
avoid over-modification of the model. This iterative process continued until all model parameters
and key fit indices met recommended criteria. Following this procedure, NAV3 was removed
from navigation construct, S4 was removed from the speed construct, and IRC1 was removed
from the interaction construct1.
The adequacy of the measurement model for website usability is evaluated based on
model-data fit and the magnitude of first-order factor loadings on the second-order website
usability factor. Two types of model-data fit indices are used to evaluate the goodness of fit of
the model: absolute fit and incremental fit indices (Hair et al. 1998). First, two measures of
absolute fit (which determine the degree to which the overall model predicts the observation
correlation matrix) were used: chi-square statistic and the root mean square error of
approximation (RMSEA). To show a good fit, the chi-square statistic needs to be non significant
(i.e., no difference between actual and predicted matrices) and RMSEA values below 0.50
suggest good model fit and values between 0.50 and 0.80 suggest acceptable model fit. Second,
two measures of incremental fit (which compares the proposed model to some baseline model)
were used: the adjusted goodness-of-fit index (AGFI) and the non-normed fit index (NNFI).
NNFI and AGFI indices greater than 0.90 suggest adequate model fit and indices greater than
1 Once items removed, the reliability coefficients were 0.85, 0.91, and 0.77 for ease-of-navigation, speed, and
interactivity respectively.
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0.95 suggest good model fit. Finally, loadings on the second-order factor above 0.60 are
considered acceptable (Bagozzi and Yi 1988).
As illustrated in Figure 2, an excellent fit was obtained (X2= 26.18, p = 0.7128; RMSEA
= 0.040, AGFI = 0.96, NNFI = 0.99). Each of the items loaded strongly on the appropriate factor,
and the three factors were significantly correlated with each other. Hypotheses 1a, 1b, and 1c
were supported since the paths between first-order factors and website usability were significant
(p< 0.05).
FIGURE 2. Results of Second-Order Confirmatory Factor Analysis
Finally, in order to test Hypothesis 2, a linear regression was performed using website
usability as independent variable and attitude as dependent variable. Results provide support for
Hypothesis 2 since the coefficient (Std Beta = 0.79, and t = 19.43) was positive and significant
(Table 4).
Website
Usability
Ease-of-
Navigation Speed Interactivity
NAV1 NAV2 NAV4 S1 S2 S3 IRC2 IRC3
0.84
0.81
0.62
0.79 0.86 0.78 0.89 0.89 0.86 0.81 0.77
0.37 0.26 0.39 0.22 0.21 0.25 0.34 0.40
Chi-Square = 26.18, df = 17, p-value = 0.7128, RMSEA = 0.040, AGFI = 0.96, NNFI = 0.99
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TABLE 4. Regression Results for Predicted Path Relationships
Dependent variable Independent variables Std Beta R square t p
Attitude Usability 0.79 0.53 19.43 0.00
In addition, a final regression was performed to test the direct effects of each first-order factor on
the attitude toward the website. As expected, all three factors had positive relationships with the
attitude toward the site. Ease-of-navigation had the largest effect on attitude, followed by
interactivity, and speed (Std Betas = 0.499, 0.305, and 0.115 respectively; all p-values < 0.05).
DISCUSSION
Summary of Findings
With the explosive growth in consumer electronic commerce (Hoffman & Novak, 2000)
and Internet-enabled organizations (Straub & Watson, 2001), appropriate metrics that not only
evaluates website quality but also provide managers with insights into potential problems areas is
urgently needed (Agarwal & Venkatesh, 2002). This research developed and validated a
multidimensional measure of website usability in a retailing context. Results suggested that the
proposed website usability measurement scale has satisfactory psychometric characteristics.
First, results suggested that each of the dimensions of the scale (ease-of-navigation, speed, and
interactivity) is reliable. Second, results from factor analyses provided evidence of construct
validity. Finally, based on the Technology Acceptance Model, the scale also showed evidence of
predictive validity by being positively correlated with participants’ attitude toward the website.
Implications for Research
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To our knowledge, this research is the first to propose a reliable, valid, and parsimonious
perceived website usability measurement scale. This scale is easy to administer (8 items) and it
also provides specific information to researchers by being multi-dimensional. Future research
should be performed to test the generalizability of the proposed measurement scale to other
online contexts. For instance, it would be interesting to use the measurement scale to assess the
usability of informational websites destined at consumers or business websites destined at
professional buyers instead of consumers.
Implications for Practice
A website should be designed so that users can easily accomplish the task they want to
accomplish, or find the information they need. Since many users are unable to complete the task
they wanted to accomplish on a website (Kalczynski, Senecal, & Nantel, 2006), it is quite
important for managers to be able to investigate and find which characteristics of the website are
appreciated and those that are not. This scale will be useful in pinpointing specific usability
dimensions of a website (ease-of-navigation, speed, and interactivity) that need to be improved.
For instance, a website could be perceived by users as fast and easy to navigate, but as lacking
interactivity. Thus, managers could envision solutions such as given opportunities for users to
customize their experience on the website.
Limitations
The main goal of this study was to develop a short, reliable, and valid perceived website
usability measurement. The first limitation of this research is its limited generalizability. Only
one website was used to develop and test our proposed usability measurement scale and only one
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segment of Internet users (i.e., undergraduate students) participated in our data collection. As
mentioned, additional research should be conducted to validate the proposed usability scale using
other websites and types of Internet users. The second limitation of this research is that we
cannot be certain that the respondents experienced all the interactivity functions available on the
website investigated, thus their answers to the interactivity items may be underestimated. Finally,
the proposed measurement scale is based on subjective measures of usability. It would also be
possible to assess website usability with objective measures such as scenario completion time,
successful scenario completion rate, and time spent recovering errors (Whiteside, Bennett, &
Holtzblatt, 1988). In addition, the end users connection speed can also be a factor while
measuring speed dimension. Future research should measure what Internet access was available
to respondents (T-1 line, dial-up, etc.) while addressing speed. Thus, similarly to the work of
Straub, Limayem, and Karahanna-Evaristo (1995) and Barnett et al. (2006) it would be
interesting to compare objective and subjective usability measures in future research.
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APPENDIX
Construct Items Item Descriptions Sources
Ease-of-
Navigation
NAV1 On this website, it is simple to accomplish the
task I want to accomplish.
Nielsen (1999)
NAV2 I find the website easy to use. Loiacono,Watson,&
Goodhue (2002)
NAV3 It is easy to find the information I need. Lewis (1995)
NAV4 It was easy to learn to use the website. Lewis (1995)
Speed
S1 The speed in which the computer provided
information was fast enough.
Palmer (2002)
S2 The rate at which the information was
displayed was fast enough.
Palmer (2002)
S3 The website loads quickly. Loiacono,Watson,&
Goodhue (2002)
S4 The pages download quickly on this website. Nielsen (1999)
Interactivity
IRC1 The website offers customization. Palmer (2002); Barnes
& Vidgen (2001)
IRC2 The website can treat you as a unique person
and respond to your specific needs.
Agarwal and Venkatesh
(2002)
IRC3 The website provides content tailored to the
individual.
Barnes and Vidgen
(2001)
IRC4 The website provides adequate feedback to
assess my progression when I perform a task.
Tilson, Dong, Martin, &
Kiele (1998)
Attitude
ATT1 This website makes it easy for me to build a
relationship with this company.
Chen & Wells (1999)
ATT2 I am satisfied with the service provided by
this website.
Chen & Wells (1999)
ATT3 I feel comfortable in surfing this website. Chen & Wells (1999)
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