Measuring Perceived Website Usability

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Measuring Perceived WebsiteUsabilityJianfeng Wang a & Sylvain Senecal ba Department of Business & Economics , MansfieldUniversity of Pennsylvania , Mansfield, PA, 16933 E-mail:b Department of Marketing , HEC Montreal , Montreal,Quebec, Canada , H3T 2A7 E-mail:Published online: 14 Aug 2009.

To cite this article: Jianfeng Wang & Sylvain Senecal (2007) Measuring Perceived WebsiteUsability, Journal of Internet Commerce, 6:4, 97-112, DOI: 10.1080/15332860802086318

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Measuring Perceived Website Usability

Jianfeng WangSylvain Senecal

ABSTRACT. The objective of this research was to develop a short, reli-able, and valid perceived website usability measurement scale. A sam-ple of 350 participants was used to collect the necessary data.Exploratory and confirmatory factor analyses were performed topurify the proposed scale. Analysis indicated that the proposedmulti-dimensional usability scale is reliable and shows evidence ofconstruct and predictive validity. Academic and managerial impli-cations were discussed. [Article copies avail-able for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH.E-mail address: <[email protected]> Website: <http://www.HaworthPress.com> � 2007 by The Haworth Press. All rights reserved.]

KEYWORDS. Website usability, ease-of-navigation, speed, interactivity,user attitude

INTRODUCTION

The experience consumers have on a website is increasingly becom-ing an important topic both for academia (Agarwal & Karahanna, 2000;Novak, Hoffman & Yung, 2000) and for organizations using websites

Jianfeng Wang is Assistant Professor of Marketing, Department of Business & Eco-nomics, Mansfield University of Pennsylvania, Mansfield, PA 16933 (E-mail: [email protected]).

Sylvain Senecal is Associate Professor of Marketing, Department of Marketing,HEC Montreal, Montreal (Quebec), Canada H3T 2A7 (E-mail: [email protected]).

Journal of Internet Commerce, Vol. 6(4) 2007Available online at http://jicom.haworthpress.com� 2007 by The Haworth Press. All rights reserved.

97doi:10.1080/15332860802086318

doi:10.1080/15332860802086318

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to market their products and services. The website design is an impor-tant 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 us-ing it and before they have spent any money on potential purchases.”Thus, developing sites that are easy to use and that meet organizationalneeds is critical for organizations. One construct that may be useful inevaluating websites and consequently developing better websites isusability.

The objective of this research is to develop a short, reliable, andvalid perceived usability measurement scale. The aim is to develop aparsimonious scale that can be used across websites. Thus, the mea-surement scale could be used for benchmarking purposes within an or-ganization and/or across organizations. For instance, an organizationcould measure consumers’ perception of its website usability and oftheir competitors’ websites in order to benchmark their website withthe competition. The development of a usability measurement scalethat shows evidence of reliability and construct validity would also beuseful to researchers in order to investigate the relationship betweenperceived usability and other relevant constructs such as attitude towardthe website and intention to revisit the website (Cook & Campbell,1979; Straub, 1989).

THEORETICAL BACKGROUND AND HYPOTHESES

Usability and Functionality

The notion of usability is a key theme in the human-computer inter-action (HCI) literature. Research in the HCI tradition has long assertedthat the study of human factors is crucial to the successful design andimplementation of technological devices. The overarching goal of a ma-jority of the HCI work has been to propose techniques, methods, andguidelines for designing better and more “usable” artifacts. Drawingupon cognitive frameworks of human-computer interaction groundedin psychology, prior research developed user models that delineated thecognitive 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 websiteusability. A system is said to be functional when it provides functions

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needed by users to perform their tasks (Goodwin, 1987). A website canbe evaluated based on the presence or absence of certain functions or onthe performance of those functions. However, past research has foundthat users’ acceptance of a system is contingent not only on its function-ality but also on its usability (Davis, 1986; Goodwin, 1987).

The concept of usability can be defined as “how well and how easilya user, without formal training, can interact with an information systemof a website” (Benbunan-Fich, 2001). Bernard et al. (1981) suggestedthat a “truly usable system must be compatible not only with the charac-teristics of human perception and action, but, most critically, with us-ers’ cognitive skills in communication, understanding, memory, andproblem solving.” A usability evaluation consequently assesses the easeof use of a website functions and how well they enable users to performtheir tasks efficiently. Thus, usability is a more inclusive construct thanfunctionality.

Usability Metrics

A variety of alternative approaches to usability evaluation havebeen proposed in prior work. Melody et al. (2001) identify five dis-tinct approaches: testing, inspection, inquiry, analytical modeling,and simulation. Among these approaches, one common characteristicof usability evaluation methods is their dependence on subjective as-sessments in the form of user judgments. Thus, usability is not intrinsi-cally objective in nature, but rather is closely intertwined with anevaluator’s personal interpretation of the artifact and his or her inter-action with it (Agarwal & Venkatesh, 2002). Although self-reportedmeasures are commonly used, research shows that perceived ease ofuse of a system is strongly correlated to subjective system usage mea-sures, but weakly correlated to objective system usage measures (Straub,Limayem, & Karahanna-Evaristo 1995; Barnett et al., 2006).

Research has been ongoing in identifying approaches to improve on-line usability (Boling, 1995; Levi & Conrad, 1996; Nantel & Senecal,2007; Palmer, 2002; Pitkow & Kehoe, 1996). Studies often focus onthe 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, Nanteland Senecal (2007) suggest that there is a positive relationship betweenthe time users spend waiting for webpages to download and the proba-bility that they will complete their task on the website. Other research isbased on Microsoft Usability Guidelines (MUG). Five major categories

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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 (com-munity, personalization, refinement), and emotion (challenge, plot,character strength, pace) (Agarwal & Venkatesh, 2002; Venkatesh &Ramesh, 2006; Venkatesh & Agarwal, 2006).

To date, the literature has conceptualized usability as either a unidi-mensional construct or a multidimensional construct composed of twodimensions (Table 1). Except for Palmer (2002), most research has notexplored usability as a construct composed of more than two dimen-sions. Based on the current literature, we suggest that usability is com-posed of at least three dimensions: ease-of-use navigation, speed, andinteractivity. Table 1 provides a summary of the research on the threemain dimensions used to assess the usability construct.

Of the various factors that contribute to usability of a website, ease ofnavigation has been deemed important by a majority of researchers (seeTable 1). Ease of navigation relates to the level of time and effort re-quired to accomplish specific tasks (Venkatesh, 2000). Good naviga-tion design helps users acquire more of the information they are seekingand makes the information easier to find. Thus, a key challenge in build-ing a usable website is to develop a good navigational structure and ap-propriate hyperlinks. Ease-of-navigation is analogous in essence to the


TABLE 1. Usability Metrics Used in Prior Research

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ease of use in IT research (Davis, Bagozzi, & Warshaw, 1989), but it isspecific to website navigation.

According to several authors download delay is also an important de-sign criterion on the Internet (see Table 1). Speed is important since it en-ables users to attain their goals without too much wait. Dalleart and Kahn(1999) argued that consumers were able to separate the evaluation ofwaiting experiences from the evaluation of the website. However, theyalso show that when there is uncertainty about the waiting (as with themajority of downloads), the negative feeling generated by the waiting ex-perience carries over to the evaluation of the website. They suggest thatwaiting for the homepage to download was less damaging to the websiteevaluation than having to wait during the interaction with the website.Their study revealed that delays shorter than expected led to better evalu-ations of the website. In addition, Sismeiro and Bucklin (2004) suggestthat there is a negative relationship between downloading time for a webpage and the probability of requesting an additional web page within awebsite. It has to be noted that since the focus of this study is on elementsthat a website can control, the objective measure of download delay willnot be taken into account; only user perception will be assessed.

When users choose to use a technology, they are also choosing to inter-act with that technology (Orlikowski, 2000). A key capability of theInternet is its capacity to support greater interaction for users (Palmer,2002). Interactivity can be defined as a characteristic of a computer-medi-ated communication in the marketplace that increases with the bidirection-ality, timeliness, mutual controllability, and responsiveness of communica-tion as perceived by consumers and firms (Yadav & Varadarajan 2005).For instance, interactivity can be used to make the website personalizable.Venkatesh and Ramesh (2006) argue that the ability to customize websitesis an important design characteristic because it helps users save time andprovides information that is of greatest interest to them. Thus, as suggestedby several authors (see Table 1), we suggest that website interactivity isalso an underlying dimension of website usability.

Research Model and Hypotheses

Our conceptual framework is presented in Figure 1. Based on theliterature review, we propose that ease-of-navigation, speed, and inter-activity are three underlying dimensions of the usability construct. Theyare first-order factors that share a common variance that reflects a singlesecond-order factor labeled as website usability. Thus, the followinghypothesis is posited.


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H1: Website usability is a single second-order factor with three first-or-der 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.

As suggested in the HCI literature, technological artifacts that aremore usable are likely to change user’s cognitions, thus engender posi-tive attitudes. Usable systems not only meet the instrumental goals ofusers, but also alleviate the cognitive effort associated with use (Niel-sen, 2000). Based on empirical results from the Technology AcceptanceModel (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989), a usablewebsite should generate a positive attitude toward it (see Figure 1).Thus, a positive correlation should exist between the usability constructand attitude toward the website. A positive relationship would provideevidence of predictive validity of our measurement scale.

H2: There is a positive correlation between consumers’ perceivedwebsite usability and their attitude toward the website.

METHODOLOGY

Data

The sample was composed of 350 undergraduate business students.Each student was asked to go to a specific transactional website (www.


FIGURE 1. Conceptual Framework

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eddiebauer.com) and perform the following simple tasks: (1) Find andread about Eddie Bauer’s Children Privacy Policy; (2) Search and selecta sweater that he/she would like to buy, add it to shopping cart, but donot check out; (3) Find the Eddie Bauer 3-in-1 car seat for the children,add to the shopping cart, but do not check out; and (4) Remove thesweater and the car seat from the shopping cart, and exit the website.Then, they were asked to complete a paper-pencil questionnaire. Thequestionnaire was used to assess their perception of the website’s us-ability 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, Watson, & Goodhue,2002), and interactivity (Agarwal & Venkatesh, 2002; Palmer, 2002;Tilson, Dong, Martin, & Kiele, 1998; Barnes & Vidgen, 2001) wereused to assess each dimension of website usability. The following crite-ria were used to select scale items: (1) items had to focus on a single di-mension, not bridge two or more dimensions, a feature critical fordiscriminant validity, (2) they had to use, or be adaptable to, a commonformat for ease of administration (i.e., a seven-point Likert scale). Theitems used for each dimensions are presented in the Appendix. Anadapted version of attitude toward the website scale (Chen & Wells,1999) was used to assess participants’ attitude toward the website (seeAppendix for specific items).

RESULTS

The objective of the analysis was to examine the measurement scalereliability and initial construct validity of the three-dimensional websiteusability measurement scale. First, descriptive statistics and initial reli-ability estimates were computed. Second, an exploratory factor analy-sis was performed to test the proposed structure of the measurementscale and to purify the scale by eliminating items if necessary. Third, aconfirmatory factor analysis was performed with the remaining itemsto verify the scale and test that usability is a second-order constructwith a more robust test. Finally, a regression analysis was performed totest the relationship between perceived usability and attitude toward thewebsite.


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Descriptive Statistics and Reliability Estimates

Table 2 gives univariate statistics and correlations among the websiteusability items. In general, participants reported a fairly strong sense ofwebsite usability. More importantly, in general correlations betweenitems from the same dimension (see triangles in Table 2) were higherthan correlations between items from two different dimensions. Theonly exception was item IRC4 which showed higher correlation withitems not in the interactivity dimension. The Cronbach’s alpha coeffi-cients are 0.88, 0.94, and 0.80 for navigation, speed and interactivity re-spectively. Meanwhile, the Cronbach’s alpha coefficient for attitudemeasurement scale was 0.77.


TABLE 2. Univariate Statistics and Pearson Correlations Among UsabilityItems*

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Exploratory Factor Analysis

Items were then submitted to an exploratory factor analysis usingprincipal components as means of extraction and varimax as method ofrotation. Three factors, displayed in Table 3, emerged. Speed explained53% of the variance, Ease-of-Navigation 12%, and Interactivity 10%.As recommended for exploratory work by Nunnally (1967), only load-ings above 0.60 are displayed. As suspected following the correlationanalysis (Table 2), IRC4 did not meet the 0.60 threshold and was re-moved. All three factors had eigenvalues greater than one and individu-ally explained a significant portion of the usability variance.

Hypothesis Testing

A second-order confirmatory factor analysis (CFA) was conductedto assess the discriminant validity of the usability items and the contri-bution of the three dimensions to the overall construct of usability (Hy-pothesis 1). We used Structural Equation Modeling (SEM) (Lisrel 8.72software) to perform the CFA.

Following Sethi and King (1994), iterative modifications were madefor each of the constructs by observing modification indices and coeffi-cients to improve key model fit statistics. Further, as recommended byJoreskog and Sorbom (1989), only one item was altered at a time to


TABLE 3. Exploratory Factor Analysis Results

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avoid over-modification of the model. This iterative process continueduntil all model parameters and key fit indices met recommended crite-ria. Following this procedure, NAV3 was removed from navigationconstruct, S4 was removed from the speed construct, and IRC1 was re-moved from the interaction construct.1

The adequacy of the measurement model for website usability isevaluated based on model-data fit and the magnitude of first-order fac-tor loadings on the second-order website usability factor. Two types ofmodel-data fit indices are used to evaluate the goodness of fit of themodel: absolute fit and incremental fit indices (Hair et al., 1998). First,two measures of absolute fit (which determine the degree to which theoverall 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 nonsignificant (i.e., no difference between actual and predicted matrices)and RMSEA values below 0.50 suggest good model fit and values be-tween 0.50 and 0.80 suggest acceptable model fit. Second, two mea-sures of incremental fit (which compares the proposed model to somebaseline model) were used: the adjusted goodness-of-fit index (AGFI)and the non-normed fit index (NNFI). NNFI and AGFI indices greaterthan 0.90 suggest adequate model fit and indices greater than 0.95 sug-gest good model fit. Finally, loadings on the second-order factor above0.60 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). Eachof the items loaded strongly on the appropriate factor, and the three fac-tors were significantly correlated with each other. Hypotheses 1a, 1b,and 1c were supported since the paths between first-order factors andwebsite usability were significant (p < 0.05).

Finally, in order to test Hypothesis 2, a linear regression was per-formed using website usability as independent variable and attitude asdependent variable. Results provide support for Hypothesis 2 since thecoefficient (Std Beta = 0.79, and t = 19.43) was positive and significant(Table 4).

In addition, a final regression was performed to test the direct effectsof each first-order factor on the attitude toward the website. As ex-pected, all three factors had positive relationships with the attitude to-ward the site. Ease-of-navigation had the largest effect on attitude,followed by interactivity, and speed (Std Betas = 0.499, 0.305, and0.115 respectively; all p-values < 0.05).


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DISCUSSION

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 butalso provide managers with insights into potential problems areas is ur-gently needed (Agarwal & Venkatesh, 2002). This research developedand validated a multidimensional measure of website usability in a re-tailing context. Results suggested that the proposed website usabilitymeasurement scale has satisfactory psychometric characteristics. First,results suggested that each of the dimensions of the scale (ease-of-navi-gation, speed, and interactivity) is reliable. Second, results from factoranalyses provided evidence of construct validity. Finally, based on theTechnology Acceptance Model, the scale also showed evidence of pre-dictive validity by being positively correlated with participants’ attitudetoward the website.


FIGURE 2. Results of Second-Order Confirmatory Factor Analysis

TABLE 4. Regression Results for Predicted Path Relationships

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Implications for Research

To our knowledge, this research is the first to propose a reli-able, valid, and parsimonious perceived website usability measure-ment scale. This scale is easy to administer (8 items) and it alsoprovides specific information to researchers by being multi-di-mensional. Future research should be performed to test the gen-eralizability of the proposed measurement scale to other onlinecontexts. For instance, it would be interesting to use the measure-ment scale to assess the usability of informational websites destinedat consumers or business websites destined at professional buyersinstead of consumers.

Implications for Practice

A website should be designed so that users can easily accomplishthe task they want to accomplish, or find the information they need.Since many users are unable to complete the task they wanted to ac-complish on a website (Kalczynski, Senecal, & Nantel, 2006), it isquite important for managers to be able to investigate and findwhich characteristics of the website are appreciated and those thatare not. This scale will be useful in pinpointing specific usability di-mensions of a website (ease-of-navigation, speed, and interactivity)that need to be improved. For instance, a website could be perceivedby users as fast and easy to navigate, but as lacking interactivity.Thus, managers could envision solutions such as given opportunitiesfor users to customize their experience on the website.

Limitations

The main goal of this study was to develop a short, reliable, andvalid perceived website usability measurement. The first limitationof this research is its limited generalizability. Only one website wasused to develop and test our proposed usability measurement scaleand only one segment of Internet users (i.e., undergraduate students)participated in our data collection. As mentioned, additional researchshould be conducted to validate the proposed usability scale using otherwebsites and types of Internet users. The second limitation of this re-search is that we cannot be certain that the respondents experienced allthe interactivity functions available on the website investigated, thustheir answers to the interactivity items may be underestimated. Fi-


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nally, the proposed measurement scale is based on subjective mea-sures of usability. It would also be possible to assess website usabilitywith objective measures such as scenario completion time, success-ful scenario completion rate, and time spent recovering errors (White-side, Bennett, & Holtzblatt, 1988). In addition, the end users connectionspeed can also be a factor while measuring speed dimension. Futureresearch should measure what Internet access was available to re-spondents (T-1 line, dial-up, etc.) while addressing speed. Thus, sim-ilarly to the work of Straub, Limayem, and Karahanna-Evaristo(1995) and Barnett et al. (2006) it would be interesting to compareobjective and subjective usability measures in future research.

NOTE

1. Once items removed, the reliability coefficients were 0.85, 0.91, and 0.77 forease-of-navigation, speed, and interactivity respectively.

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RECEIVED: January 10, 2007REVISED: May 31, 2007

ACCEPTED: July 28, 2007


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APPENDIX

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Documents

Measuring Perceived Website Usability