The Effect of Experience on System Usability Scale Ratings


Longitudinal studies have to do with testing over time and thus take into consideration previous user experience with a product or product versions. However, it is difficult to conduct these types of studies. Therefore the literature is sparse on examples of the explicit effect of user experience on user satisfaction metrics in industry-standard survey instruments. During a development experience in 2009, we used a cross-sectional method to look at the effects of user profiles on ratings for commercial products that use one such instrument, the System Usability Scale or SUS.

Recent research has reported finding that differences in user ratings could be based on the extent of a user’s prior experience with the computer system, a Web site being visited or a desktop application like Microsoft’s Office suite being used. Compared to off-the-shelf office products or personal Web applications, we were curious if we would find the same experience effect for domain specialists using geosciences products in the course of their daily professional job roles. In fact, from data collected with 262 end users across different geographic locations testing two related oilfield product releases, one Web-based and one desktop-based, we found results that were quite close to early assessment studies: Users having a more extensive experience with a product tended to provide higher, more favorable, SUS scores over users with either no or limited experience with a product—and by as much as 15-16%, regardless of the domain product type. This and other observations found during our product testing have led us to offer some practical how-to’s to our internal product analysts responsible for managing product test cycles, administering instruments like the SUS to users, and reporting results to development teams.

Practitioner’s Take Away

Given a potential relationship between such factors as user experience and SUS ratings, we provided the following instructions to our internal company practitioners using SUS as a measure of usage satisfaction. We believe these same recommendations would be useful for anyone using the SUS with domain products.

  • Ask users for their level of experience with the domain product being evaluated with the SUS.
  • Regularly inspect the literature for other demographics (or survey changes) that might be useful to incorporate formally across their product centers using SUS as part of user test instruments.
  • Report SUS results from the intended user community for a product.
  • Be explicit to users about directions for correctly filling out the SUS.
  • Be explicit to administrators about how to correctly administer the SUS to users.
  • Be explicit to product teams about directions for appropriately reading the results of the SUS.


A quick look at the human-computer interaction
literature shows a few recent studies dealing with the longitudinal aspect
of usability evaluation—that is, testing over time to take into
consideration previous user experience with a product or product versions.
For example, testing users over an 8-week period and recording frustration
episodes and levels, Mendoza and Novick (2005) found that users’
frustration levels decreased significantly over the duration of the study
as proficiency levels increased. In a 2005 ACM article entitled "Does Time
Heal? A Longitudinal Study of Usability," Kjeldskov and his co-authors
reported similarly that, in relation to problem severity, there was "a
significant difference between the mean severity ratings for novices and
experts, with the latter generally experiencing the usability problems of
the system as less severe” (Kjeldskov, Skov, & Stage, 2005, p.190).
Performing tasks repeatedly with two comparable products, this time over a
period of a few days, Vaughan and Dillon (2006) suggested that product
comprehension, navigation, and usability were also useful measures for
uncovering performance differences between designs over time.

The renewed interest in longitudinal usability
stems, in part, from a concerted effort—of real, practical benefit to
product development teams iteratively designing and reviewing interfaces
with customers—to understand implications for factors such as user
profiles for testing, review methodologies in company development
processes, or strategies for usability results analysis. Those who may
have attended the 2007 ACM SIGCHI conference workshop entitled “Capturing
Longitudinal Usability: What really affects user performance over time?”
would have heard this concern voiced: “Typical usability evaluation
methods tend to focus more on ‘first-time’ experiences with products that
may arise within the first hour or two, which trends the results more
towards ‘discoverability’ or ‘learnability’ problems, rather than true
usability problems that may persist over time” (Vaughan & Courage, 2007,
pp. 2149-2150).

Software intent and target user base should always
have implications for test participant selection. For example, some
software may only be intended to be used infrequently by first-time users
(such as Web-based IT systems, installation programs, etc.) and should
typically support novices by being fast and easy to learn and use. Other
applications, such as some of our own oilfield domain applications, are
designed for more frequent use and for highly experienced domain experts.
These applications boast features that may take a longer time to learn to
use but, over the long run, support expert users in being more effective
in doing particular work.

Specifically tasked with assisting product
development teams in iteratively designing, evaluating, and quantifying
the user experience for suites of product interfaces, our software
analysts have used standard survey instruments like Questionnaire for User
Interaction Satisfaction (QUIS; Harper & Norman, 1993) and SUS (Brooke,
1996) for quantitative information about product satisfaction to
supplement results from more direct product review methods. In 2009, we
collected data from 262 users of two oilfield products we were developing.
These users had varying degrees of experience with the product and thus
allowed us to examine the effects of experience on usability ratings.
Further, we were able to explore whether these effects differed by the
domain products being evaluated.

Lewis (1993) reported finding differences in user
ratings on a questionnaire similar to SUS, the Computer System Usability
Questionnaire (CSUQ), stemming from the number of years of experience
these users had with the computer system. More recently, Sauro (2011a)
found, from over 1,100 users visiting some 62 Web sites (airlines, rental
cars, retailers, and the like), that users who had been to the Web site
previously rated these Web sites as much as 11% more usable than those who
had never been to these Web sites prior to rating them with SUS. His
examination of 800 users with varying years of usage of common, commercial
desktop products like Word, Quicken, Photoshop, and the like found the
identical average difference based on experience—in general, “a user with
a lot of prior experience will rate an application as more
usable…especially…the case between the users with the most experience and
those with the least (or none at all)” (Sauro, 2011b, p.1)

Compared to off-the-shelf office products or personal
Web applications, we were curious if we would find an experience effect
for domain specialists using geosciences products in their professional
job roles.

Method and Process

The following sections discuss the method,
evaluation measures, participants, and results of our study.


The System Usability Scale (SUS) is a simple,
widely used 10-statement survey developed by John Brooke while at Digital
Equipment Corporation in the 1980s as a “quick-and-dirty” subjective
measure of system usability. The tool asks users to rate their level of
agreement or disagreement to the 10 statements—half worded positively,
half negatively—about the software under review. For reporting results, we
used a scoring template that turns the raw individual survey ratings
across multiple users of a specific software product into a single SUS
score based on Brooke’s standard scoring method (manipulating statement
ratings to get them a common 0-4 rating, then multiplying the sum by 2.5
to get a score that can range from 0-100). We used such tools with reviews, regardless of whether we were
looking at interface designs or implementations.

The results of our study were from one 2009 testing
cycle for two related products from the same suite: one with a Web-based
frontend and the other, a desktop application. The SUS questionnaire was
administered by one of our product commercialization teams and the
associated deployment team—teams responsible for conducting internal
testing and training or coordinating external prerelease (beta) testing
with customers. The SUS was given to users at the end of an iteration
period, which could last one week or much longer.

The SUS surveys were provided in English for these
tests. Because both internal and external user populations come from any
number of countries with non-native English speakers, we asked users
upfront to let us know if any part of the survey instruments was unclear
or confusing, and we examined individual user scores after the test for
any potential problems resulting from misunderstanding or inadvertent

The SUS survey included requests for demographic
information from users: their name, their company, their job role, the
software being evaluated, the software version, date of the user’s
evaluation, duration of the evaluation, and the user’s experience using
the software. The survey then provided the following 10 standard
statements with 5 response options (5-point Likert scale with anchors for
Strongly agree and Strongly disagree):

  1. I think that I would like to use this system frequently
  2. I found the system unnecessarily complex
  3. I thought the system was easy to use
  4. I think that I would need the support of a technical person to be able to use this system
  5. I found the various functions in this system were well integrated
  6. I thought there was too much inconsistency in this system
  7. I would imagine that most people would learn to use this system very quickly
  8. I found the system very cumbersome to use
  9. I felt very confident using the system
  10. I needed to learn a lot of things before I could get going with this system


Typically, to evaluate the SUS responses, we look
at the mean and standard deviations of the user responses for a specific
system. We then color code individual responses in the scoring template to
help visualize positive, neutral, and negative responses, accounting for
the alternating positive-then-negative makeup of the statements.

With cases that had responses that were remarkably high
or low, we contacted users directly and reviewed their responses with them
to confirm their intentions were correctly captured. Figure 1 shows one
example representative of what we found—here, one of several users had an
overall SUS rating far lower than all others on the same product (User
Ev.5). The user had responded as if all statements were positively worded,
despite prior instructions on filling out the survey. We used a simple
color-coding scheme. For positively worded statements, where a higher
number means a higher rating, we assigned green to 5 or 4, yellow to 3,
and orange to 2 or 1. For negatively worded statements, the color codes
were reversed: orange for 5 or 4, yellow for 3, and green for 2 or 1. We
did this so that we could more easily compare ratings for an individual
user or across users. As seen in Figure 1, every other statement for one user has an orange color code
indicating a negative rating—a possible indication that users forgot to
reverse their responses. This is similar to Sauro’s findings who noted
that users sometimes “think one thing but respond incorrectly” (2011a,
p.102). In fact, Sauro and Lewis’ research found that approximately 13% of
SUS questionnaires likely contain mistakes (2011). Similar to Sauro and
Lewis’ findings, 11% of our SUS questionnaires likely contained mistakes.
In cases where we thought the SUS scores were in error—we contacted
individual users to go over their responses with them. In the case shown
below, the user’s positive overall comment about the product’s much
deserved usability also made us question the user’s SUS score, and this
was verified when we spoke to the user over the phone later.

Figure 1

Figure 1. Example of user miscue in SUS scoring instrument


Participants were actual users of our software. A total of 262 users responded, 190 for the first product and 72 for the second. Prior to their familiarizing themselves with and using the new product software versions, all users were asked to identify themselves as one of the following:

  • Someone who had never used (or installed/configured as the case may have been) the software
  • Someone who had some but limited experience using the software
  • Someone who had extensive experience using the software

Figure 2 shows the experience level of the users tested. Approximately the same number of users from different locations were given the SUS after a set period of training and subsequent testing and use of the product.

Figure 2

Figure 2. Number of users in each experience level for both products


A 3 by 2 (Experience-Extensive, Some, Never by Product-One and Two) between subjects factorial ANOVA was conducted to determine the effects of experience and product type on usability ratings. As seen in Figure 3, SUS scores increased based on experience level, and this effect was significant,
F (2, 256) = 15.98, p < 0.001, n2 = 0.11. There was no main effect of product
F (1, 256) = 3.57, p = 0.06) nor was there an interaction between product and experience,
F (2, 256) = 0.46,
p = 0.63. Table 1 provides the results of a Tukey’s HSD pairwise comparison for post-hoc analysis. This table shows that the Extensive group had higher ratings than both the Never and Some groups (both
p < 0.001), and that there was no significant difference between the Some and Never groups (p = 0.117).

Table 1. Results of Pairwise Comparison for Three Difference Experience Levels

Table 1

Figure 3

Figure 3. SUS scores across products and experience levels: There was a main effect of experience but no effect of product or interaction between experience and product. Error bars represent the 95% confidence interval.


Despite its age, compared to other industry survey
tools measuring user satisfaction, SUS still shows itself to be a useful,
practical quantitative tool for supplementing more direct observations or
reviews about software use (Tullis & Stetson, 2004).

That said, SUS ratings are influenced by several
factors in addition to the usability of the product being evaluated. There
are factors like user experience that our own practical experience shows
can dramatically affect overall SUS scores for domain products—in fact, by
as much as 15-16% between our Never and Extensive groups for either
product type. This is consistent with results from an assessment by Sauro,
who reported that more experienced users of Web sites (a repeat user group
who had been to the Web site before) tended to provide higher, more
favorable, SUS scores over a first-time user group (those who’d never been
to the Web site before)—on average, experience increased scores very close
to our results—by 6-15% (2011a, p.96).

We should add that other factors may also affect
satisfaction ratings—for example, inherent differences resulting from
cultural diversity of users (Tractinsky, 1997), mistakes in understanding
SUS terminology for non-English speaking users (Finstad, 2006), or upfront
deployment and setup processes that may be part of product testing.
Studies suggest, too, that shorter total deployment time or the existence
of product upgrades or patches are not only good predictors that users
will observe a failure leading to a software change (Mockus, Zhang, & Luo
Li, 2005) but also turn up as clear support factors directly associated
with overall product satisfaction (Shaw, DeLone, & Niederman, 2002).
Others explicitly identify “ease of software installation” as a
significant determinant factor in its own right for product selection
(Erevelles, Srinivasan, & Ragnel, 2003; Kolo & Friedewald, 1999).


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