misq2007 a task-based model of perceived website complexity

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Nadkarni & Gupta/Task-Based Model of Perceived Website Complexity

MIS Quarterly Vol. 31 No. 3, pp. 501-524/September 2007 501

RESEARCH ARTICLE

A TASK-BASED MODEL OF PERCEIVED

WEBSITE COMPLEXITY1

By: Sucheta Nadkarni

College of Business AdministrationUniversity of Nebraska, Lincoln

Lincoln, NE 68588-0491

U.S.A.

[email protected]

Reetika Gupta

College of Business and Economics

Lehigh University

Bethlehem, PA 18015

U.S.A.

[email protected]

Abstract

In this study, we propose that perceived website complexity

(PWC) is central to understanding how sophisticated features

of a website (such as animation, audio, video, and rollover

effects) affect a visitor’s experience at the site. Although pre-

vious research suggests that several elements of perceived

complexity (e.g., amount of text, animation, graphics, range

and consistency of webpages configuring a website, ease of

navigating through it, and clarity of hyperlinks) affect impor-

tant user outcomes, conflicting results yielded by previous

1Deborah Compeau was the accepting senior editor for this paper. Sue

Brown was the associate editor. Dov Te'eni, Vicki McKinney, and Alex

Ramirez served as reviewers.

Earlier versions of this paper were presented at the INFORMS Conference,

San Antonio, TX, 2000; American Marketing Association Educators’

Conference, Orlando, FL, 2002; and the OCIS Division of the Academy of

Management National Conference, Seattle, WA, 2003.

research have created an important debate: Does complexity

enhance or inhibit user experience at a website? In this study, we draw on the task complexity literature to develop a

broad and holistic model that examines the antecedents and

consequences of PWC. Our results provide two important

insights into the relationship between PWC and user out-

comes. First, the positive relationship between objective

complexity and PWC was moderated by user familiarity.

Second, online task goals (goal-directed search and experi-

ential browsing) moderated the relationship between PWC

and user satisfaction. Specifically, the relationship between

PWC and user satisfaction was negative for goal-directed

users and inverted-U for experiential users. The implications

of this finding for the practice of website design are

discussed.

Keywords: Perceived website complexity, user perception,

website usability

Introduction

Businesses spend billions of dollars annually to add a wide

range of sophisticated features, such as animation, audio,

video, and rollover effects, to improve users’ experience with

their websites. However, these features are of value only

when online users find them interesting and when the user

experience at the website is satisfying. For example,

Brynjolfsson and Smith (2000) find that by providing efficient

search features at a website, online retailers can charge a

price-premium to time-sensitive customers. In contrast, when

website features inhibit information search, users do not buy

products at the website, resulting in loss of sales for online

firms (Hof 2001). Moreover, Kotha, Rajgopal, and Venkata-




502 MIS Quarterly Vol. 31 No. 3/September 2007

chalam (2004) argue that website design layout can provide

online retailers cost advantages by reducing the cost of

acquiring new customers and the need for expensive offline

support as customers navigate the site and help themselves.

In other words, website features that provide users a satisfying

experience can act as differentiators in a cluttered market

place and can provide online retailers with a sustainable com-

petitive advantage (Kotha et al. 2004). Thus, understanding

how the information cues presented at a website affect user

experience is critical for the success of online companies.

We propose that perceived website complexity (PWC) is

central to understanding how these information cues affect a

user’s experience at the website. There is evidence that

elements of PWC affect the degree to which users find a

website appealing and satisfying (Geissler et al. 2001;

Stevenson et al. 2000). This implies that by effectively

managing a website’s level of complexity, a firm can

differentiate its website from other sites and produce acompelling navigation experience for users. Drawing on

Wood’s (1986) framework of task complexity, we define

PWC as a function of three facets: component (density and

dissimilarity of visual features such as text, graphics, video,

and animation presented at a website), coordinative (range of

topics covered by the website and interrelationships between

these topics), and dynamic (ambiguity and clarity of action-

outcome relationship in a hyperlink).

Research suggests that several elements of PWC (e.g., amount

of text, animation, graphics, range and consistency of

webpages configuring a website, ease of navigating through

it, and clarity of hyperlinks) affect important user outcomessuch as perceived web-information and web-system quality

(McKinney et al. 2002), perceived ease of use (Agarwal and

Venkatesh 2002), communication effectiveness (Geissler et

al. 2001), and satisfaction (Stevenson et al. 2000). However,

a particularly important debate remains unresolved. Results

of some studies suggest that simple websites are easy to use

and effective (Agarwal and Venkatesh 2002; Shneiderman

1998), whereas others suggest that complexity increases the

richness of information presentation and thereby enhances

user satisfaction (Palmer 2002). In addition, other studies

suggest an inverted-U relationship between website com-

plexity and communication effectiveness (Geissler et al. 2001;

Stevenson et al. 2000). Because different studies present con-flicting findings, it is unclear whether complexity enhances or

inhibits user experience at a website. We attempt to address

this question by theorizing that user familiarity and online

task goals play important roles in determining how PWC

affects an important user outcome, user satisfaction.

The model of PWC developed in our study clarifies the

relationship between complexity and user outcomes in two

ways. First, we distinguish between objective website com-

plexity and PWC . Our study shows that the relationship

between objective website complexity and PWC is moderated

by user familiarity. Second, our study demonstrates that

online task goals— goal-directed (focused on information

gathering to achieve a predetermined end goal) and experien-

tial (focused on information browsing for recreation and navi-

gational experience) (Hoffman and Novak 1996; Schlosser

2003; Te'eni and Feldman 2001)—are important in under-

standing the relationship between PWC and user satisfaction.

We show that the PWC–user satisfaction relationship is

different for goal-directed and experiential users as the two

online task goals induce users to adopt separate mechanisms

in interacting with the online environment. An important

implication of our results is that web designers need to create

websites that can accommodate different levels of complexity

in order to maximize the satisfaction of both goal-directed and

experiential users. Taken as a whole, the comprehensive set

of antecedent and outcome relationships of PWC examined in

this study can provide useful insights for web evaluation and

design.

Theory Development

Definition of Perceived Website Complexity

We use Wood’s (1986) comprehensive framework of task

complexity to define perceived website complexity. This

task-based framework is especially important for the online

environment, where users visit websites mainly to fulfill task goals such as goal-directed search and experiential browsing

(Hoffman and Novak 1996; Novak et al. 2003). Wood

contends that information cues (pieces of information about

the stimulus that individuals must process in performing a

task) are central to understanding perceived complexity. For

online users, websites are the primary medium through which

they interact with the information cues in the online environ-

ment to achieve their online task goals. Because websites

represent a major task stimulus for online users (Agarwal and

Venkatesh 2002; Venkatesh and Agarwal 2006; Hoffman and

Novak 1996; McKinney et al. 2002; Palmer 2002),

information cues (e.g., text, animation, hypertext structure,

and navigation tools) presented at a website are central toonline users’ perceptions of task complexity.

Wood specifies perceived complexity (TCt) as a linear com-

bination of three dimensions that capture distinct elements of

the information cues that make up a task stimulus: component

(TC1), coordinative (TC2), and dynamic (TC3):

TCt = "TC1 + $TC2 + (TC3




MIS Quarterly Vol. 31 No. 3/September 2007 503

Perceived component complexity refers to the users’ per-

ceptions of the density and dissimilarity of information cues

in the task stimulus. A task stimulus with dense and dis-

similar information cues is perceived as more complex than

one with sparse information cues. For a website, dense cues

are represented by long text, many images, and colors;

whereas dissimilarity is reflected in the use of varied formats

(e.g., text, graphics, and animation) such as dissimilar

graphics and dissimilar information items on a webpage.

Perceived coordinative complexity describes users’ percep-

tions of the range of and interdependencies among the dif-

ferent information clusters (groups or chunks of related

topics) in the task stimulus. The wider the range of informa-

tion clusters and interrelationships among the clusters, the

greater the perceptions of coordinative complexity. For a

website, high coordinative complexity is reflected in a wide

range of topics covered by the website, high number of

webpages configuring a website, and many paths linking

webpages.

Perceived dynamic complexity refers to the ambiguity

(number of different possible interpretations of the same piece

of information) and uncertainty (clarity of action–outcome

relationships) that individuals face in performing a task.

Ambiguous hyperlinks and search procedures and unpre-

dictable click streams can increase the dynamic complexity of

a website.

Previous literature posits that simultaneous sources of

complexity, from all of the elements of a stimulus, are percep-

tually integrated to produce a general level of perceived

complexity (Berlyne 1960; Wood 1986). Thus, we treat PWC

as a unified aggregate construct and test our model for the

aggregate PWC rather than for individual facets of PWC

(component, coordinative, and dynamic).

Objective Complexity and Perceived Complexity

The literature on task complexity distinguishes between

objective and perceived complexity of a task stimulus

(Campbell 1988; Earley 1985; Te'eni 1989, 2001; Wood

1986). The objective complexity is defined by the number

and configuration of information cues in the stimulus itself,

whereas perceived complexity, which is based on the indi-

vidual’s perception of the stimulus, focuses on the person–

stimulus interaction. The web evaluation literature also treats

objective and perceived complexity as distinct constructs

capturing different facets of websites. Objective complexity

is defined by a universal set of design characteristics that

encompass the technological aspects of a website (e.g., pre-

sentation formats, multimedia and search tools, hierarchical

menu structure, and download time for webpages) and has its

roots in the system design literature (Bucy et al. 1999; Nielsen

2000; Schubert and Selz 1998; Shneiderman 1998). In

contrast, perceived complexity is rooted in the human–

computer interaction (HCI) literature and captures users’

personal interpretation of the website and their interaction

with it (e.g., how uncertain and ambiguous users find the

hyperlinks, how dense and dissimilar users find the infor-

mation cues presented at a website) (Agarwal and Venkatesh

2002; McKinney et al. 2002; Te'eni 1989, 2001). The central

tenet of the perceived complexity literature is that users may

perceive the same level of objective complexity differently

because of their different backgrounds and experience.

PWC and Web Evaluation Outcomes

Web evaluation studies suggest that elements of component

(e.g., amount of text, animation, and graphics on webpages),

coordinative (e.g., range and consistency of webpages con-

figuring a website), and dynamic complexity (e.g., ease of

navigating through it and clarity of hyperlinks) that make up

PWC determine important user outcomes such as web-

information and web-system quality (McKinney et al. 2002),

perceived ease of use (Agarwal and Venkatesh 2002; Davis

1989; Venkatesh 2000; Venkatesh et al. 2003), communica-

tion effectiveness (Geissler et al. 2001), and satisfaction

(Stevenson et al. 2000). However, the varied interpretations

resulting from these studies have produced controversy as to

whether PWC increases or hinders user satisfaction at a

website. One stream of research suggests that PWC increases

the richness of information presentation, thereby providing a

rich and satisfying experience (Hall and Hanna 2004; Nack et

al. 2001; Palmer 2002). Other research suggests that PWC

creates confusion and frustration in users, resulting in a

negative impact on key user outcomes such as perceived ease

of use (Agarwal and Venkatesh 2002; Shneiderman 1998;

Venkatesh 2000; Venkatesh et al. 2003). A third stream

suggests an inverted-U relationship between PWC and user

outcomes, such that low levels of PWC create boredom for

users, whereas high levels of PWC create confusion and con-

flict for users (Geissler et al. 2001; Stevenson et al. 2000).

Thus, this research posits that medium levels of PWC maxi-

mize user satisfaction by arousing users’ curiosity and

engaging them in the navigation process without excessively

burdening them.

We address this theoretical conflict regarding website com-

plexity by developing a broad and holistic model that inte-

grates objective website complexity, PWC, user familiarity,

online task goals, and user satisfaction. We discuss this

model in the following section.





Objective

Website

Complexity

H1

H2

ComponentComplexity

PWC

Online task goals:

Goal-directed vs.

Experiential

Coordinative

Complexity

Dynamic

Complexity

User

Satisfaction

H3, H3a, H3b

User

Familiarity

Objective

Website

Complexity

H1

H2

ComponentComplexity

PWC

Online task goals:

Goal-directed vs.

Experiential

Coordinative

Complexity

Dynamic

Complexity

User

Satisfaction

H3, H3a, H3b

User

Familiarity

Figure 1. Theoretical Model of Perceived Website Complexity

Hypotheses

Drawing on the task complexity literature (Campbell 1984,

1988; Campbell and Gingrich 1986; Early 1985; Wood 1986),

we develop a model (shown in Figure 1) that examines the

antecedents and consequences of PWC. We propose that

(1) PWC mediates the relationship between objective com-

plexity and user satisfaction, (2) user familiarity moderates

the relationship between objective complexity and PWC, and(3) online task goals moderate the relationship between PWC

and user satisfaction. Thus our model addresses how user

familiarity and online task goals determine the varying rela-

tionships between objective complexity, PWC, and user

satisfaction. We discuss each of these propositions in the

following sections.

Objective Website Complexity and PWC

The task complexity literature contends that perceived com-

plexity mediates the relationship between objective complexity and task outcomes. Thus, it is perceived and not

objective complexity that directly affects user outcomes.

Objective complexity of a task influences the cognitive load

associated with performing the task—the information pro-

cessing effort that individuals need to spend in order to see

and understand information cues in the task stimulus

(Campbell 1984, 1988; Campbell and Gingrich 1986; Early

1985; Lindsay and Norman 1977; Wood 1986). This cogni-

tive load translates into the perceived complexity of the

information cues in the task environment.

When individuals face information cues, they spend cognitive

resources to encode the cues and decide how to respond to

these cues (Lindsay and Norman 1977). Objectively complex

cues require significantly more cognitive resources to encode

and respond to than simple cues. Because users have limitedcognitive resources, the excessive information processing

demands of objectively complex cues may create cognitive

overload. The notion of cognitive load (Lindsay and Norman

1977) is consistent with Miller’s (1956) seminal work on “the

magical number seven, plus or minus two,” which suggests

that human working memory can hold up to seven bits of

information, plus or minus two, at one time. This limitation

of human information processing in short-term memory

requires that displays be kept simple by minimizing anima-

tion, wild background patterns, and contrasting text colors;

that multiple page displays be consolidated; and that window-

motion frequency be reduced (Nielsen 1994; Shneiderman1998). Thus, simple layout, clear content, and straightforward

navigation procedures reduce the cognitive strain on users.

When users experience less cognitive strain in interacting

with a website, they are likely to find it less complex.

Hypothesis 1 : Objective website complexity will be

positively related to PWC.





User Familiarity

The task complexity literature suggests that the relationship

between objective complexity and perceived complexity is a

function of the individual’s familiarity with the task stimulus

and/or task domain (Campbell 1988; Earley 1985; Huber 1985; Jacoby et al. 1971; Keisler and Sproull 1982; Taylor

1981). Familiarity increases individuals’ tolerance of com-

plexity by (1) allowing them to better understand inter-

relationships between elements of the task stimulus and

(2) helping them to distinguish relevant from irrelevant

information in the task stimulus.

Individuals familiar with the task stimulus or domain have

superior knowledge of a given task stimulus than others have,

which allows them to develop a better understanding of the

relationships between different elements of that task stimulus

(Cox and Cox 1988; Hong et al. 2002; Russo and Johnson1980). Individuals familiar with the task stimulus or domain

can encode new information in the task stimulus more effi-

ciently than other individuals can. Results of recent studies

support the conclusion that the richer store of knowledge

possessed by users familiar with the web contributes to a

clearer and better understanding of the content, organization,

and browsing procedures of the website or the products

offered than is possible for individuals who are unfamiliar

with the web (Agarwal and Venkatesh 2002; Cox and Cox

2002). Thus, for the same level of objective website com-

plexity, users familiar with the websites or products available

at the website may experience less PWC than unfamiliar

users.

A critical facet of processing task-related information is the

ability to select relevant information while ignoring infor-

mation irrelevant to the task at hand (Berlyne 1970; Larkin et

al. 1985). Individuals familiar with a task stimulus or task

domain may use their domain knowledge to limit their atten-

tion to task-related information, thus minimizing their

cognitive efforts toward redundant information at a website.

Therefore, we expect that familiarity will moderate the

relationship between objective complexity and PWC.

Hypothesis 2: User familiarity will moderate the

relationship between objective website complexity

and PWC such that, for the same level of objective

website complexity, the PWC ratings of users

familiar with the website or products available at a

website will be lower than those of users not familiar

with the website or products available at a website.

PWC, Online Task Goals, and User Satisfaction

The concept of user satisfaction occupies a central position in

information systems as well as web evaluation research.

Information systems studies have shown that when users are

satisfied with a system, they are more likely to use the system(Delone and McLean 1992). User satisfaction is also central

to website evaluation research (Palmer 2002; Te'eni and

Feldman 2001). When users experience satisfaction at a

website, they are likely to return to the website (Hoffman and

Novak 1996; Te'eni and Feldman 2001), purchase products at

the website, and recommend the website to others (McKinney

et al. 2002). Conversely, when users are dissatisfied with the

website, they are likely to develop a negative impression of

the website, which is likely to hurt the overall image of the

website and online sales through the website. In other words,

user satisfaction is critical to website success. Thus, we chose

user satisfaction as the outcome in our model of PWC.

We posit that online task goals will play a significant role in

determining the relationship between PWC and user satis-

faction with the website. Researchers have classified online

task goals into two distinct categories: goal-directed and

experiential (Hoffman and Novak 1996; Novak et al. 2003;

Schlosser 2003; Te'eni and Feldman 2001). A goal-directed

activity consists of using the Internet for its informative value

and purchase utility, such as directly searching for specific

information to accomplish a task or to reduce purchase uncer-

tainty, whereas an experiential goal refers to browsing the

website in a relatively unstructured manner for recreational

purposes. Research suggests that goal-directed and experien-

tial task goals induce users to adopt separate mechanisms as

they interact with the online environment (Hoffman and

Novak 1996; Schlosser 2003), implying that PWC may inter-

act with online task goals to modify users’ satisfaction with

the website.

Goal-directed users have a clearly definable goal hierarchy,

putting more effort into reaching the end goal rather than into

undirected exploration. Goal-directed users consider chal-

lenge a deterrent to their main effort, as they do not want to

expend unnecessary effort in processing challenging infor-

mation (Wolfinbarger and Gilly 2001). As the complex cues

in the environment shift goal-directed users’ attention away

from their end-goals, medium and high levels of PWC may

pose a challenge to these users. Therefore, we predict that the

distraction experienced by goal-directed users at medium and

high levels of PWC may decrease their satisfaction at the

website. On the other hand, goal-directed users will experi-





2002). After completing the pilot questionnaire, each respon-

dent reviewed all questions for content, clarity, meaning-

fulness, and the ability to measure the construct (Bagozzi

1980). We also used item-total correlation and discrimination

based on the t-statistic to eliminate redundant items (Churchill

1979). Items with low item-total correlations (two items:

0.25, n.s. and 0.19, n.s.) and nonsignificant t-statistic (same

two items: 1.07, n.s. and 1.24, n.s.) between the ratings of

respondents above 74th percentile and those below the 26th

percentile were eliminated. Based on these results of the pilot

test, we retained 20 items for the testing phase of the study.

In the pilot phase, we also conducted a preliminary analysis

of the dimensionality and validity of other construct measures

before using them in the testing phase. In the exploratory

factor analyses on a sample of 80 subjects, consistent with

previous literature, the user familiarity construct yielded two

dimensions (factor 1: eigen value = 3.24, factor loadings =

0.82, 0.86; factor 2: eigen value = 2.84, factor loadings =0.84, 0.88) explaining 79 percent of the variance. Consistent

with our conceptualization of PWC, the 20 items of PWC

yielded three dimensions—component (eigen value = 4.51),

coordinative (eigen value = 3.67), and dynamic (eigen value

= 2.98)—that explained 84 percent of the variance. The

factor loadings on each factor ranged from 0.82 to 0.93.

Finally, consistent with McKinney et al. (2002), the user

satisfaction scale yielded a single factor (eigen value = 3.95)

explaining 78 percent of the variance in the pilot study. The

results provide preliminary evidence of validity for our

construct measures.

Testing Phase

In the testing phase, we collected data from 332 under-

graduate students at a major eastern (170) and a major

midwestern (162) university to test our hypotheses. Most of

the subjects (67 percent) were 18 to 24 years old and 43

percent of the subjects were female. All subjects had over 3

years of experience in using the Internet, and their weekly

Internet use ranged from 4 to 10 hours. To ensure that results

were not idiosyncratic to student subjects, we replicated the

study with subjects drawn from the population at large,

collecting data from 120 nonstudent subjects (male: 75,

female: 45) belonging to the age groups 9 to 14 years (40)

and 30 to 60 years (80). Subjects, who regularly visited the

library in the local community, comprised a cross-section of

people from different educational backgrounds and had an

average age of 42. We used the unpaired t-test (n = 452) to

determine whether there were significant differences in our

study variables between the two samples. There were no dif-

ferences in the results obtained with the two samples (com-

ponent complexity: t = 1.14, n.s.; coordinative complexity:

t = 1.05, n.s.; dynamic complexity: t = 0.97, n.s.; product

familiarity: t = 1.18, n.s.; website familiarity: t = 1.03, n.s.;

user satisfaction: t = 0.95, n.s.). Therefore, we combined the

two samples to test our model (n = 452).

We used the same procedures for all subjects. Student

volunteers were recruited for the study by circulation of a

notice describing the experiment to students from the sections

of a senior-level required course in business policies and of a

junior-level required introductory course in marketing (parti-

cipation rate: 89 percent). Nonstudent volunteers were

recruited by circulating the notice to visitors of a local com-

munity public library (participation rate: 78 percent). We

collected the data in a computer laboratory setting for all

subjects by using Dell computers with Windows XP, and

comparable hard drive space and RAM.

Participants were randomly assigned to one of two online task goal conditions (goal-directed and experiential) and one of

three objective complexity conditions (high, medium, and

low). Each participant was given an instruction sheet that

explained the specific task assigned (goal-directed or

experiential) and that instructed him/her to visit the site for 20

minutes (based on previous web evaluation studies and pilot

results), to complete the task assigned, and to complete the

questionnaire (which had been placed in a sealed envelope

under the instruction sheet) after completing the task.

Objective Website Complexity Conditions

We used a series of steps to select websites representing high,

medium and low levels of objective complexity. First, we

randomly selected from Yahoo! Directories 150 websites

representing 10 product categories: travel, auction, entertain-

ment, news or media, sports, computer hardware components,

office software, auto, astronomy, and fertilizers. Using mul-

tiple stimuli (e.g., websites) to represent the treatment cells

decreases the likelihood of skewed results and potential

confounds and increases the robustness and generalizability

of the results (Aaker 1997; Cox and Cox 2002).

Based on a thorough review of the web design literature, we

identified 13 objective complexity metrics that capture the

underlying technological characteristics of website design

(hypertext structure and presentation) and that are relevant to

component (e.g., percentage of white space, graphics count

and size, word count, color count), coordinative (e.g., number

of webpages, average depth of pages, average internal and

external, and same page links on the webpage, and coefficient

of variation in the number of presentation forms such as text,





graphics, and video), and dynamic complexity (e.g., average

pop-up advertisements per webpage, average webpage down-

load time, number of support tools such as site map, search

option and help links) (Bucy et al. 1999; Nielsen 2000;

Palmer 2002; Schubert and Selz 1998; Shneiderman 1998).

We acknowledge that objective complexity measures do not

have a perfect mapping with measures of component, coordi-

native, and dynamic complexity because, as discussed earlier,

objective complexity and PWC have roots in different streams

of literature and encompass different assumptions about com-

plexity. However, we chose objective complexity measures

that were relevant to the three facets of PWC. For example,

the objective complexity measures of “pop-up advertise-

ments,” “page download time,” and “support tools” are rele-

vant to dynamic complexity; the higher the number of pop-up

advertisements and page download time, the higher the

uncertainty (dynamic complexity) associated with the links on

the webpages. On the other hand, the higher the support tools

available at a website, the lower the uncertainty associated(dynamic complexity) with the links on the webpage.

We used a web metrics software package to calculate the 13

complexity measures. We then computed an aggregate mea-

sure of objective complexity2 (coefficient " = 0.79) based on

the mean z scores of the 13 variables. Such formative treat-

ment of objective complexity is consistent with the web

design literature cited above. We then classified the 150

websites into high (top 33.33 percentile: 50 websites),

medium (middle 33.33 percentile) and low (bottom 33.33

percentile) objective complexity levels.

Thirty experienced web designers rated the degree to whicheach of the ten objective metrics makes the website complex

(seven-point scale: not complex at all, 1; neutral, 4; highly

complex, 7) based on “universal web design principles” to

define robust factor levels. Each website was rated by three

designers. There was a high correlation between the objective

metrics and designers’ ratings (ranging from 0.59, p < 0.001

to 0.91, p < 0.0001) for the 150 websites.

We selected the final websites in two stages. First, the web-

sites’ level of complexity were categorized as high, medium,

and low based on objective metrics as well as on ratings of the

designers. Second, websites were chosen so that the number

of websites in each complexity level was equal for the three

categories. This process yielded a total of 48 websites—16 in

each complexity level. The 48 websites represented eight

product categories: travel, auction, news or media, sports,

computer hardware components, office software, auto, and

astronomy. We developed a categorical variable of objective

website complexity (high, medium, and low) rather than con-

tinuous individual measures to ensure an adequate number of

subjects per treatment cell (Churchill and Surprenant 1982).

We used ANOVA and mean-difference tests to confirm that

there was a significant difference in aggregate objective

complexity metrics measured across the three levels (F =

19.21, p < 0.001).

Online Task Goal Conditions

We designed the two online task goal conditions—goal-

directed and experiential—based on existing, valid manipu-lations (Novak et al. 2003; Schlosser 2003). Those assigned

to a goal-directed task condition were instructed to go to their

site with “the goal of efficiently finding something specific

within that site” (Schlosser 2003, p. 188). Those assigned to

the experiential task goal condition were instructed to “have

fun, looking at whatever you consider interesting or enter-

taining.” What to look for was not specified, so that goal-

directed and experiential users would have similarly hetero-

geneous information needs. Thus, both goal-directed and

experiential users could adapt their experience to match their

own information or entertainment needs (Schlosser 2003).

To ensure that users viewed the website in the light of the

assigned online task goal, we designed a scale of manipu-

lation check for the two task conditions based on codes

constructed by Novak et al. (2003). We show the items in the

manipulation check scale in Table 1. Goal-directed users

reported a significantly lower score (M = 2.71) than experi-

ential users (M = 3.80; F = 29.17; p < .001) on the mani-

pulation scale, suggesting that they were more focused and

had an identifiable purpose. The scale was an agree–disagree

scale where 1 suggested “high goal-directedness” and 7 “low

goal-directedness.”

Measures

Perceived Website Complexity

We used the 20-item scale developed in the pilot phase to

measure PWC. We show the 20 items and the supporting

literature in Table 1 and illustrate them graphically in

Appendix A .

2We also conducted partial least squares analyses in which we measured

objective website complexity as follows, which is in line with our definition

of PWC: TCt = "TC1 + $TC2 + (TC3. We then classified the 48 websites into

high (top 33.33 percentile), medium (middle 33.33 percentile), and low

(bottom 33.33 percentile). Because this classification of websites based on

PLS analyses matched completely the classification based on the objective

complexity measure based on the mean z-scores of the 13 metrics, we do not

include this analysis in the paper.





Table 1. Theoretically Derived Measurement Scales of Component, Coordinative, and Dynamic

Complexity

Construct

Definition

Theoretical

Dimensions Website Complexity Measures of Each Theoretical Dimension Source of Items

Component

Complexity:

The degree to

which users

find the form

and content

cues at the

individual

webpages

visually dense

and dissimilar

Similarity/

dissimilarity

(Berlyne 1960)

Visual density

(Berlyne 1960;

Campbell 1988)

1. The clarity between text and images was High (1) – Low (7)

2. The images (or graphics) on the webpages were Similar (1) –

Dissimilar (7)

3. The information items on the webpages were Similar (1) –

Dissimilar (7)

4. The text on the webpages was Short (1) – Long (7)

5. The webpage backgrounds were Not visually dense at all (1) –

Visually Dense (7)

6. The graphics on the webpages were Not visually dense at all

(1) – Visually dense (7)

7. The layout of the webpages was Not visually dense at all (1) –

Visually dense (7)

Geissler et al. 2001

Stevenson et al. 2000

Coordinative

Complexity:

Users’ percep-

tions of the

range of and

the degree of

connectedness

among the

information

clusters at a

website

Range

(Campbell 1988;

Wood 1986)

Connectedness/

Interrelationships

(Steinmann

1976)

1. The range of the alternative links to find information was Broad

(1) – Narrow (7)

2. The choice of both image and text clicks was Broad (1) –

Narrow (7)

3. The variety of information clusters (groups of related

information) was Low (1) – High (7)

4. The links at the website were Logical (1) – Illogical (7)

5. The layout across the webpages was Uniform (1) – Not

Uniform at all (7)

6. The backgrounds across the webpages were Uniform (1) – Not

Uniform at all (7)

7. The information clusters (groups of related information) were

Interrelated (1) – Not at all interrelated (7)

Agarwal and Venkatesh 2002

Daft and Lengel 1986

Ha and James 1998

Kieras and Polson 1985

McKinney et al. 2002

Nielsen 2000

Palmer 2002

Schubert and Selz 1998

Shneiderman 1998

Steuer 1992

Dynamic

Complexity:

Users’ percep-

tions of ambi-

guity of hyper-

links and

uncertainty of

the relationship

between the

hyperlink and

the ensuing

webpages

Ambiguity

(Campbell 1988)

Action-outcome

Uncertainty

(March and

Simon 1958)

1. Procedures to browse the websites were Unclear (1) – Clear

(7)

2. Hyperlinks on the website were Unambiguous (1) –

Ambiguous (7)

3. Information presented on the websites was Unambiguous (1) –

Ambiguous (7)

4. Information on the succeeding links from the initial page was

Predictable (1) – Unpredictable (7)

5. Individual links took me to desired webpages: Always (1) –

Never (7)

6. Information presented on the website was Uncertain (1) –

Certain (7)

Agarwal and Venkatesh 2002


Oinas-Kukkonen 1998

Palmer 2002

Steuer 1992

Te’eni 2001

Objective

Website

Complexity

1. Percentage of white space: Percentage of a page not taken

up by text and graphics

2. Graphics count: The mean number of graphics on thewebpages

3. Graphics size: The mean size of the graphics on the

webpages

4. Word count: The mean number of words on the webpages

5. Color count: The mean number of colors on the webpages

6. Average number of different presentation forms used on a

webpage (text, graphics, video, audio, animation)

Bucy et al. 1999

Geissler et al. 2001

Nielsen 1994, 2000Palmer 2002

Perrow 1986

Schubert and Selz 1998

Shneiderman 1998

Took 1990





Table 1. Theoretically Derived Measurement Scales of Component, Coordinative, and DynamicComplexity (Continued)

Construct

Definition

Theoretical

Dimensions Website Complexity Measures of Each Theoretical Dimension Source of Items

7. Average internal, external and same page links on the

webpage8. Number of webpages configuring a website

9. Average depth of pages: The average number of pages from

a home page to a page with no more forward links or only

links external to the website

10. Coefficient of variation in the number of different presentation

forms (e.g., text, graphics, video, audio, animation) used

across webpages

11. Average pop-up advertisements per webpage

12. Average webpage download time

13. Number of support tools (e.g., site map, search option, help

links)

Familiarity Product

familiarity

Website

familiarity

1. My knowledge of the product/s served by the website is: Very

Low (1) – High (7)

2. I have used the products served by the website: Very often (1)

– Never (7)

3. My knowledge of the website is: Very High (1) – Low (7)

4. I have visited the website: Never (1) – Very often (7)

Cox and Cox 1988, 2002

Hong et al. 2002

Online Task

Goal

Manipulation

Check

Goal-directed

and Experiential

When I was at the XX website…..

1. I had a distinct identifiable purpose: Strongly agree (1) –

Strongly disagree (7)

2. I was looking up specific information: Strongly agree (1) –

Strongly disagree (7)

3. I was very focused: Strongly agree (1) – Strongly disagree (7)

4. I was absorbed in finding specific information: Strongly agree (1)

– Strongly disagree (7)

5. I was clicking often and went to many different webpages:

Strongly agree (1) – Strongly disagree (7)

6. I was absorbed in seeing where I could go next: Strongly agree

(1) – Strongly disagree (7)

7. I was randomly surfing through the website: Strongly agree (1)

– Strongly disagree (7)

Novak et al. 2003

User

Satisfaction

1. After using this website, I am: Very dissatisfied (1) – Very

satisfied (7)

2. After using this website, I am: Very displeased (1) – Very

pleased (7)

3. Using this website made me: Frustrated (1) – Contented (7)

4. After using this website, I feel: Terrible (1) – Delighted (7)

5. After using this website, I will: Never recommend it to my friends

(1) – Strongly recommend it to my friends (7)

6. After using this website, I will: Never use it again (1) – Most

likely use it again (7)






Appendix A1 illustrates websites with high and low perceived

component complexity based on visual density and dis-

similarity. The website with low component complexity has

sparse information cues (use of fewer graphics and colors)

presented in a single format (text only and content of the

items is related), whereas the one with high component com-

plexity has visually dense information cues (long text, many

graphics, and colors) presented in different formats (use of

text, graphics, and video; dissimilar graphics; dissimilarity of

content items on the same webpage—movie trailer, cooking,

news, weather, directories).

Appendix A2 illustrates websites with high and low perceived

coordinative complexity based on the range of information

clusters and the interrelationships between them. The coordi-

natively simple website includes few information clusters

(e.g., top stories, world, domestic country, business, science

technology, and sports) with few interrelationships between

information clusters (e.g., few click choices in that only head-lines can be clicked, uniformity in layout across webpages).

On the other hand, the coordinatively complex website covers

a broad range of information clusters (e.g., more news topics,

more external links to domestic and international news

sources) and many interrelationships between information

clusters (e.g., more click choices—both headlines and photos

can be clicked, layout across webpages is not uniform).

Appendix A3 demonstrates the ambiguity and uncertainty

with elements of perceived dynamic complexity. In Appendix

A3, icons that resemble the shopping carts used in real stores

allow users to clearly interpret the hyperlink, based on their

experiences in a real store, resulting in low levels of perceivedambiguity with the hyperlink. In contrast, icons that resemble

“normal bags” or a “line of interlocked shopping carts” may

produce confusion and multiple interpretations of what a

hyperlink represents, creating high levels of perceived ambi-

guity associated with the link. Similarly, in Appendix A3, by

clicking on the “Chicago Bears@Green Bay packers at

Lambeau field” link, users expect to reach a webpage where

they can place a bid for these specific tickets, as is shown in

the certain link. However, in the case of the uncertain link,

when a user reaches an external webpage (Joe’s Green Bay

Packers’ corner) that does not allow him/her to place the bid,

the individual action-outcome expectancy is not met. This

increases users’ perception of uncertainty associated with this

hyperlink.

The final design of the questionnaire, including clarity and

specification of instructions, choice of rating scales, reverse

coding and sequencing of items, was based on suggestions to

reduce method bias (Torangeau 1999; Williams and Anderson

1994).

User Familiarity

The literature has identified two measures of familiarity:

knowledge/expertiseand exposure (Cox and Cox 2002; Hong

et al. 2002; Jacoby et al. 1971; Johnson and Russo 1984;

Russo and Johnson 1980). We measured product familiarity

by the user’s knowledge of the product and frequency of

product use and we measured website familiarity by the user’s

knowledge of the website and frequency of visits to it. Based

on previous research and results of the pilot study, we defined

user familiarity as a higher order factor comprising two

dimensions: product familiarity and website familiarity (Law

and Wong 1998).

User Satisfaction

We adapted the six-item scale of user satisfaction developed

by McKinney et al. (2002) (shown in Table 1). This scale isespecially useful for our study for three reasons. First, it was

developed specifically for websites, which is the focus of our

study. Second, it measures users’ overall satisfaction with the

website rather than their satisfaction with specific attributes

of the website. Third, the psychometric properties of this

scale indicated promising validity and reliability in a previous

study (McKinney et al. 2002) as well as in our pilot study.

Control Variables

We controlled for several variables that may serve as alterna-

tive explanations of variance in PWC and user satisfaction.First, a potential alternative explanation of PWC and user

satisfaction is initial likeability. Grush (1976) suggests that

likeability of initially liked stimulus tends to improve with

exposure, whereas that of initially disliked stimulus declines.

We also controlled for the users’ liking of the products served

by the website. For example, users who are movie enthusiasts

are likely to find a movie site usable regardless of website

complexity (perceived or objective) and user familiarity.

Thus, in our empirical analyses, we controlled for the initial

liking of the website and the products it serves. Because the

two measures were highly correlated (r = 0.61, p < 0.001), we

created a composite measure of initial liking.

We also controlled for four individual demographics (gender,

age, education, and Internet experience) that have been shown

to affect users’ perceptions of and attitudes toward computer-

related technologies in general and websites specifically.

Several HCI and web evaluation studies have shown that

males and females interact differently with, and have different

attitudes toward, computer-related technologies in general





(Harrison and Rainer 1992; Wilder et al. 1985) and websites

in particular (Venkatesh and Agarwal 2006), and these dif-

ferences may lead to differences in perceptions of website

complexity and user satisfaction. Studies have also shown

that younger users hold more positive attitudes toward com-

puters than do older users (Harrison and Rainer 1992;Venkatesh and Agarwal 2006), which may lead to differences

in perceptions of website complexity and user satisfaction

between older and younger users. Several HCI studies have

found that education is positively related to favorable com-

puter attitude and negatively related to computer anxiety

(Harrison and Rainer 1992; Igbaria and Parasuraman 1989).

Thus, users with higher education are likely to be more

tolerant of complexity and find the websites more satisfying

than users with lower education levels. Finally, because

Internet experience positively affects attitudes toward web-

sites and anxiety in navigation (Hong et al. 2002), experi-

enced Internet users are likely to be more tolerant of com-

plexity and find complex websites more satisfying than lessexperienced users.

Data Analyses

We used partial least squares to analyze the data (Chin 1998;

Venkatesh and Morris 2000). The categorical measures of

objective website complexity, online task goals, and the inter-

action variables in our model violate the multivariate

normality required by maximum likelihood estimation. PLS

is particularly useful for our study because it is robust to non-

normal data distribution (Chin 1998).

We used the measurement variables to generate first order

factor scores (objective complexity, product familiarity, web-

site familiarity, component complexity, coordinative com-

plexity, dynamic complexity, and user satisfaction). We esti-

mated second order factor scores for PWC and user

familiarity using the repeated indicators method based on the

hierarchical component model suggested by Wold (1981).

This method is especially suited for our study because it can

estimate formative indicators such as those of PWC and user

familiarity and works best when the number of indicators is

approximately equal for each construct (which is the case for

the subconstructs of both PWC and user familiarity) (Chin et

al. 2003).

Before estimating the structural models, we created three

latent interaction variables (LIVs): objective complexity ×

familiarity, PWC × online task goals, and PWC² × online task

goals. First, to reduce inflation in path coefficients, we stan-

dardized and centered the indicators of each construct (Chin

1998). Then, in line with the work of Chin et al. (2003), we

represented LIVs by creating all possible products from the

two set of indicators. Finally, we used the LIV, objective

website complexity × familiarity, to estimate the interaction

effect of familiarity and we used the LIVs, PWC × online task

goals, and PWC² × online task goals, to estimate the inter-action effect of online task goals in the structural model (Chin

et al. 2003; Kenny and Judd 1984).

Results

Assessment of Measures

The descriptive statistics of the constructs are shown in

Table 2. As mentioned earlier, results of unpaired t-tests sug-

gested no significant differences between the student and the

nonstudent samples.

We assessed the reliability of individual items by inspecting

the loadings of the items on their corresponding construct

(Chin 1998) and their internal consistency values (Fornell and

Larcker 1981). As shown in Table 3, all measures satisfied

requirements for reliability (reliability greater than 0.70). The

internal consistency values for all constructs (Table 3) exceed

the 0.70 guideline that Nunnally (1978) recommends.

We assessed the discriminant validity of the first-order con-

structs by assessing their cross-loadings on other constructs.

The range of cross-loadings shown in Table 3 is considerably

lower than the corresponding factor loadings. These results

support the discriminant validity of all first order construct

measures.

For the second-order factor of PWC, the structural coeffi-

cients of component (0.85), coordinative (0.83), and dynamic

complexity (0.75) were considerably higher than the recom-

mended value of 0.70 (Chin 1998). The two subdimensions

of product (0.87) and website (0.81) familiarity also loaded

highly on the second-order factor, user familiarity. The inter-

factor correlations (component–coordinative: 0.35; compo-

nent–dynamic: 0.37; coordinative–dynamic: 0.34) were con-

siderably lower than the structural coefficients of PWC.

Similarly, the inter-factor correlations between website and

product familiarity (0.45) were lower than the structural

coefficients of user familiarity. Collectively, these results

provide evidence of reliability and validity for the higher

order factors.





Table 2. Descriptive Statistics and Cross-Sample Differences in Study Constructs

Constructs

Descriptive Statistics

Unpaired t-test(n = 452)

Undergraduate Sample(n = 332)

Library Sample(n = 120)

Mean SD Mean SD t-value

1. Component website complexity 4.05 1.89 4.12 1.95 1.14

2. Coordinative website complexity 4.21 1.75 4.15 2.01 1.51

3. Dynamic website complexity 4.29 1.94 4.32 1.76 0.79

4. Product familiarity 4.22 1.55 4.17 1.63 1.01

5. Website familiarity 4.11 1.73 4.24 1.88 0.86

6. User satisfaction 4.54 1.92 4.43 2.17 0.97

Table 3. Loadings, Cross-Loadings, and Reliability of First-Order Factors

Items Factor Loadings

Range of Factor

Cross-Loadings

Reliability

(n = 452)

1. Component website complexity (COMP) 0.87COMP1COMP2COMP3COMP4COMP5COMP6COMP7

0.880.890.850.900.870.830.91

0.21 – 0.320.25 – 0.390.19 – 0.290.24 – 0.350.21 – 0.370.17 – 0.410.25 – 0.440.28 – 0.36

2. Coordinative website complexity (COOD) 0.92

COOD1COOD2COOD3COOD4COOD5

COOD6COOD7

0.850.890.820.890.81

0.890.84

0.21 – 0.390.16 – 0.420.25 – 0.400.28 – 0.370.22 – 0.410.18 – 0.33

0.15 – 0.420.17 – 0.32

3. Dynamic complexity (DYN) 0.85

DYN1DYN2DYN3DYN4DYN5DYN6

0.820.850.810.790.830.88

0.29 – 0.350.22 – 0.380.27 – 0.430.18 – 0.290.25 – 0.360.20 – 0.320.33 – 0.45

4. Product Familiarity (PFAM)PFAM1PFAM2

0.910.87

0.15 – 0.400.22 – 0.38

0.89

5. Website Familiarity (WFMA)WFAM3

WFAM4

0.85

0.82

0.29 – 0.42

0.23 – 0.32

0.84

6. User Satisfaction (SAT) 0.92

SAT1SAT2SAT3SAT4SAT5SAT6

0.880.850.840.870.890.91

0.18 – 0.390.24 – 0.430.21 – 0.390.26 – 0.330.19 – 0.310.24 – 0.360.28 – 0.42

Because they are products of other items, indicators for interaction terms are typically not included in a confirmatory factor analysis of the measure-ment model. Their inclusion would violate assumptions about the item’s independence (Yang Jonson 1998).





Assessment of Structural Model 3

The intercorrelations among our study constructs are shown

in Table 4, whereas the results of our PLS analyses are shown

in Table 5. We discuss these results in the following sections.

Antecedents of PWC

Table 5 presents the results for the antecedents of PWC.4

None of the control variables were significantly related to

PWC (R² = 0.09). Objective website complexity (B = 0.31,

p < 0.001) had a positive relationship with PWC, supporting

hypothesis 1. Familiarity (B = –0.22, p < 0.05) had a negative

relationship with PWC. The significant negative interaction

terms of objective website complexity × familiarity (B =

–0.27, p < 0.001) suggests that the lower the familiarity, the

higher the PWC for a given level of objective website com-

plexity. These results support H2. Figure 2 depicts theserelationships graphically for familiar and unfamiliar users

(split on the median rating: 4).The graphs indicate that there

are no differences in slopes of familiar and unfamiliar users

between low and high complexity levels, indicating no inter-

action effects of familiarity. However, there are differences

in the slopes of familiar and unfamiliar users in moving from

low to medium levels of PWC and from medium to high

levels of PWC. This suggests that the interaction effects of

familiarity are driven by medium levels of PWC.

Outcomes of PWC

We tested the mediating effect of PWC between objective

website complexity and user satisfaction in two separate

models. In the first model, we tested the direct effect of

objective website complexity on user satisfaction. The results

of this analysis are shown in Part B of Table 5. There is an

inverted-U relationship between objective website complexity

and user satisfaction as is indicated by the significant negative

objective website complexity square term (B = –0.16, p <

0.05). Moreover, the significance of the objective website

complexity square × online task goals suggests that online

task goals moderate this inverted-U relationship (B = –0.22,

p < 0.05).

Second, we tested the full model of objective website com-

plexity, PWC, online task goals, and user satisfaction,5 which

is shown in Part C of Table 5. The results show that once the

PWC variables are entered in the model, the effect of objec-

tive complexity on user satisfaction becomes insignificant,

confirming that objective website complexity influences user

satisfaction through PWC. The negative PWC² term (B =

–0.17, p < 0.05) suggests an overall inverted-U relationship

between PWC and user satisfaction. The significant negative

interaction term—PWC² × online task goals (B = –0.32, p <

0.001)—confirms the moderating effect of online task goals

in the relationship between PWC and online task goals, sup-

porting H3. This moderating effect is depicted graphically in

Figure 2; a negative linear relationship is seen between PWC

and user satisfaction for experiential users, and an inverted-U

relationship between PWC and user satisfaction for goal-

directed users. These results support H3a and H3b.

Discussion

This research was motivated by an interest in examining how

and why PWC affects the user outcomes in an online environ-

ment. To this end, we developed and tested a holistic model

comprising objective website complexity, user familiarity,

PWC, online task goals, and user satisfaction. Specifically,

by focusing on user familiarity and online task goals, we

attempted to resolve the theoretical debate regarding the

nature of the relationship between complexity and user out-

comes. Not only does our model synthesize and integrate

research on complexity, it extends this body of work byclarifying howPWC affects user satisfaction. Our results pro-

vide two important insights into the relationship between

PWC and user outcomes. First, the positive relationship

between objective complexity and PWC was moderated by

user familiarity. Second, online task goals moderated the

relationship between PWC and user satisfaction. Specifically,

the relationship between PWC and user satisfaction was nega-

tive for goal-directed users and inverted-U for experiential

users. We discuss the theoretical and practical implications

of these results in the following sections.

Limitations

Limitations that circumscribe the interpretation of our

findings must be acknowledged. First, our conceptualization

3We conducted PLS analyses separately for each dimension of PWC: com-

ponent, coordinative, and dynamic complexity. The results of each indi-

vidual dimension of PWC were consistent with the overall model of PWC.

4We compared the full model including interactions (objective website

complexity × familiarity) with the model without the interaction term. The

variance explained increased significantly by adding the interaction term

()R² = 0.14, p < 0.001).

5Similarly, we compared the full model including the online task goals ×

PWC interaction with the model without this interaction. The variance

explained increased significantly ()R² = 0.12, p < 0.001).





Table 4. Intercorrelations among Study Constructs

Constructs

Intercorrelations (n = 452)

1 2 3 4 5

1. Objective Complexity —

2. PWC 0.30*** —

3. User Satisfaction 0.24* 0.25* —

4. User Familiarity –0.07 –0.21* –0.22 —

5. Online Task Goals 0.09 –0.32*** –0.19 0.08 —

*p < 0.05 **p < 0.01 ***p < 0.001

Table 5. PLS Resultsa

PWC

Parameter Estimate (Standard Error)

A. Objective Website Complexity, Familiarity, and PWC (n = 452)

Control Variablesb

R²

Model Variables:

R²

Objective Website Complexity

Familiarity

Objective Website Complexity × Familiarity

0.09

0.25

0.31*** (0.09)

–0.22* (0.07)

–0.27*** (0.04)

B. Objective Website Complexity, Online Task Goals,c

and User Satisfaction (n = 452)

Model Variables:

R²


Objective Website Complexity²

Online Task Goals

Objective Website Complexity × Online Task Goals

Objective Website Complexity² × Online Task Goals

0.17

0.23* (0.11)

–0.16* (0.05)

0.12 (0.02)

0.19* (0.02)

–0.22* (0.06)

C. Objective Website Complexity, PWC, Online Task Goals, and User Satisfaction (n = 462)

Model Variables:

R²



Objective Website Complexity²

Online Task Goals

Objective Website Complexity × Online Task Goals

Objective Website Complexity² × Online Task Goals

PWC

PWC

PWC²

Online Task Goals

PWC × Online Task Goals

PWC² × Online Task Goals

0.23

0.12 (0.07)

–0.09 (0.05)

0.11 (0.08)

0.08 (0.04)

–0.17 (0.07)

0.28*** (0.07)

–0.17* (0.04)

0.13 (0.02)

0.27** (0.02)

–0.32*** (0.05)aWe also ran a PLS model for each dimension of PWC (component, coordinative, and dynamic complexity) to confirm that the results of the overall

model were consistent with each dimension of complexity. Because we found that the results for each individual dimension of PWC were consistent

with the overall results for the PWC construct, we do not report the results in the paper.bControl variables include age, education, gender, computer use in hours per week, computer use in years, and initial satisfaction with the website.

cOnline task goals were represented by a dummy variable: Goal-directed = 0 and Experiential = 1.

*p < .05 **p < 0.01 ***p < 0.001





1

2

3

4

5

Low Medium High


Unfamiliar

Familiar 6

7

P W C

1

2

3

4

5

Low Medium High


Unfamiliar

Familiar

Unfamiliar

Familiar 6

7

P W C

1

2

3

4

5

Low Medium High

PWC

Goal-directed

Experiential6

7

U s e r S a t i s f a c t i o n

1

2

3

4

5

Low Medium High

PWC

Goal-directed

Exper iential6

7

U s e r S a t i s f a c t i o n

Objective Website Complexity, Familiarity, and PWC PWC, Online Task Conditions, and User Satisfaction

Figure 2. Moderating Effects of Familiarity and Online Task Goals in the Antecedent and ConsequenceRelationships of PWC

of PWC was based on Wood’s (1986) framework of per-

ceived task complexity, which we chose because it compre-

hensively captured the user–task interactions that are impor-

tant to defining complexity in an online environment. Thus,

our findings are unique to this specific view of complexity.

We acknowledge that there are other frameworks that define

complexity differently. For example, complexity has been

defined by the cognitive load—the number of resources it

uses (Kramer et al. 1983; Moray 1977; Sheridan 1980;

Welford 1978). Although, we developed the relationships

between objective website complexity and PWC on the basis

of these cognitive load arguments, we did not define PWC as

cognitive load. Examining our model of PWC by using this

definition of complexity may yield some important additional

insights.

Second, we created three levels (high, medium, and low) for

our objective website complexity condition so that we had an

adequate number of subjects in each treatment cell (Churchill

and Surprenant 1982). However, because of this categoriza-

tion, we could not assess whether the 16 websites in each

category affected this result. In other words, the 16 websitesin each treatment cell could represent diverse characteristics

that could affect the results differently. Although, we used a

number of websites to achieve representativeness, we do

understand that collapsing the websites to create three levels

could have created a within-cell bias (Kirk 1982).

Third, we examined complexity at an aggregate level to

determine task outcomes, which is consistent with previous

literature on perceived complexity (Berlyne 1960; Wood

1986). However, it would be interesting in future research to

investigate the effects of each dimension of PWC (com-

ponent, coordinative, and dynamic) on user satisfaction and

the underlying processes driving these effects.

Finally, goal-directed and experiential categories represent

just one way of classifying online task goals. Other classifi-

cations of online task goals, such as transacting (purchasing)

and communicating (chatting) (Hoffman and Novak 1996),

may yield different results. A related task classification per-

tains to task uncertainty (e.g., routine or nonroutine) (Gal-

braith 1973; Perrow 1967, 1986). The antecedent and conse-

quence relationships of PWC may play out differently for

routine tasks (repetitive, predictable, well understood) and

nonroutine tasks (unique or ever changing situations, difficult

to understand).

Theoretical Implications and Future Research

Our finding that user familiarity moderated the positive rela-tionship between objective complexity and PWC is consistent

with the task complexity literature (Campbell 1988; Earley

1985; Huber 1985; Jacoby et al. 1971; Taylor 1981). Users

with relatively low familiarity experienced higher PWC than

users with higher familiarity for a given level of objective

complexity (shown in Figure 2). This difference points out

the importance of user familiarity in managing complexity in

an online environment. Although the role of user familiarity





has been emphasized in literature on information systems use

(Harrison and Rainer 1992), website evaluation research has

largely ignored the role of familiarity (an exception is Hong

et al. 2002). Our results suggest that future studies should

incorporate user familiarity in testing relationships among

web evaluation constructs in an online environment.

The second and more important set of results explains how

PWC affects user satisfaction and reconciles the conflicting

findings regarding this relationship in previous literature. The

PWC–user satisfaction relationship was negative for goal-

directed users. Because goal-directed users focus on the end-

goal and minimize their cognitive effort on the navigation

process, medium and high levels of perceived complexity

distract and frustrate them. In contrast, the inverted-U rela-

tionship between PWC and user satisfaction for experiential

users suggests that experiential users are as frustrated with

low levels of PWC as they are with high levels of PWC; their

satisfaction is maximized at medium levels of PWC. Thus,

our results address the theoretical conflict on whether com-

plexity inhibits (Agarwal and Venkatesh 2002; Shneiderman

1998; Venkatesh 2000; Venkatesh et al. 2003) or improves

(Hall and Hanna 2004; Nack et al. 2001; Palmer 2002) posi-

tive user outcomes. Our results suggest that the nature of the

relationship between PWC and user satisfaction is too com-

plex to be explained completely by a single stream of

research. The different explanations of the PWC–user out-

come relationship are not contradictory; rather, they are com-

plementary in explaining this relationship for goal-directed

and experiential users; complexity inhibits user satisfaction

for goal-directed users, whereas medium levels of complexity

enhance user satisfaction for experiential users.

Although we did not empirically test the specific processes

underlying the differences in the PWC–user satisfaction

relationships for goal-directed and experiential users, our

results do raise an interesting question: Are the processes

underlying the PWC–user satisfaction relationships different

for goal-directed and experiential users? For example, the

negative relationship between PWC and user satisfaction sug-

gests that cognitive processing mechanisms may be driving

this relationship for goal-directed users. In contrast, the

inverted-U relationship between PWC and user satisfaction

for experiential users suggests that cognitive processing

mechanisms may not completely explain how PWC affects

user satisfaction for these users. The cognitive processing

mechanisms gain prominence at high levels, but not at low

and medium levels, of PWC. Other important aspects may be

critical in explaining this relationship for experiential users:

curiosity and interest (Berlyne 1960), which capture the fun,

entertainment, and enjoyment value of the website (Hall and

Hanna 2003; Nack et al. 2001). To maximize the satisfaction

of experiential users, web designers may need to manage the

complexity of the website by balancing the cognitive pro-

cessing elements with the fun and entertainment value.

Empirical examination of this contention is an important area

for future research.

Our findings on the nature of the PWC–user satisfaction rela-

tionship also contribute to the literature on online task goals

(Moe 2003; Schlosser 2003). Online task goal studies have

examined how these goals affect the search procedures and

shopping behaviors that users adopt. However, these studies

have ignored the role of the perceived attributes of the online

task environment, such as complexity, in the relationship

between user task goals and user outcomes. Our research

shows that the fit between PWC and online task goals is an

important predictor of user satisfaction.

Further, the importance of PWC–online task goal fit sug-

gested by our study raises an interesting question: How do

goal-directed and experiential users cope with PWC? Based

on Gollwitzer’s (1999) theory of implementation intentions,

there could be two strategies of coping with complex environ-

ments: complexity-resolving and task-facilitating. As the

mechanisms driving PWC–user outcome relationships are

different for goal-directed (cognition-based processes) and

experiential (affect-based processes) users, these two cate-

gories of users may adopt different strategies to cope with

complexity in an online environment. For example, when

goal-directed users face complexity, they may employ

complexity-resolving strategies by relying on navigational

aids (e.g., tool bars, search function) available at the site to

relieve some of the cognitive load that they experience. On

the other hand, experiential users who face complexity may

be motivated to resolve this complexity on their own and may

adopt task-facilitating strategies where they continue their

exploration in an undirected manner. Examining these

differences in coping strategies adopted by goal-directed and

experiential users is an important area of future research.

Practical Implications

The results of our study have some important practical impli-

cations. We found that user satisfaction for goal-directed

users was maximized at low levels of complexity, whereas

experiential users found medium levels of complexity most

satisfying. This suggests that to maximize user satisfaction,

web designers must provide different levels of complexity to

goal-directed and experiential users.

Web designers can create distinct websites with low and

medium levels of complexity in many ways. One way would

be to provide two hyperlinks for entering the site: Text Only

and Rich Graphics. Clicking on the Text Only hyperlink





could take goal-directed users to a simple website with few

graphics, little or no animation, few colors, clearly spelled out

text hyperlinks, limited information clusters, and an open

layout with adequate blank space. In contrast, experiential

users could click on the Rich Graphics hyperlink to access a

complex but visually rich website with a wide range of inter-

related information categories, rollover effects in navigation,

and audio and video formats, which could provide experien-

tial users a stimulating and enjoyable experience at the web-

site. A second way to create websites with different levels of

complexity is to provide users a login option by registering at

the website. Registered users could access more visually rich,

complex, and expanded information at the website than non-

registered users. Because goal-directed users are focused on

the goal, they would prefer to search a simple website without

logging in, whereas experiential users could use the log-in

option to access a medium complexity website.

As web designers provide higher levels of complexity toexperiential users (than goal-directed users), web designers

can use methods such as consumer profiling (Raghu et al.

2001) to identify experiential users, to determine the range of

information that may be of interest to them, and to allow

experiential users to better manage perceived complexity at a

website. Current technology available on the web such as

dynamic HTML, applets, and cookies provides the means to

acquire an individual customer profile. By use of these

applications, customers can be profiled in many ways,

including registration forms completed by users, history of

users’ actions, and current user activity. For example, a site

that sells both compact disks and books may profile a user

who always goes to the compact disks sections and never tothe book section as a music lover. The site could then provide

this user a wide range of music-related information, including

hyperlinks to external musical sites in a variety of formats

such as audio, graphics, and video. Although such richness

of form and content cues increases the complexity of the

website, it will also make the website more enjoyable to

experiential users because the information content matches

the interests of the users.

Web designers may also be able to use the type of products

served at the website to determine whether their user pool is

typically goal-directed or experiential, and may be able to

adjust the complexity of the website to maximize the satis-faction of their users. Recent research seems to suggest that

the nature of website experience desired by users may depend

on whether the online product is a search product (products

that can be evaluated based on prepurchase or pre-use

information) or an experience product (products that can be

evaluated only by using or buying the product) (Schlosser

2003). As users can gain complete information on search

products before purchase (Wright and Lynch 1995), websites

containing search product domains (e.g., a university website

with information about application procedures, specific

majors, etc.) will attract goal-directed users seeking detailed

information about the product to facilitate their decision

making. Conversely, at websites containing experiential pro-

ducts (e.g., music, wine), users are not looking at information

cues at a website to influence their purchase decision.

Instead, such websites will attract experiential users who are

seeking to enjoy the process of browsing. Because the pro-

duct category at a website determines the type of users (goal-

directed or experiential) that visit the website, web designers

can use this information to discern the optimum level of

complexity to be provided at these websites to maximize the

satisfaction of their users.

Conclusions

The overarching goal of this paper was to enrich our under-

standing of how website complexity affects important user

outcomes. We proposed perceived website complexity as a

key construct in understanding how the use of sophisticated

website design features such as animation, audio, video, and

rollover effects affect user satisfaction. The results suggest

that online task goals (goal-directed and experiential) deter-

mine how PWC affects user outcomes. Our study represents

a first step in integrating disparate explanations to develop a

more complete understanding of the complex relationships

between PWC and user outcomes. Given the undeniable

reality that complexity is inevitable as web design technology

becomes more sophisticated and as the scope of online acti-

vities expands, research that sheds light on how to manage

and adjust this complexity to maximize user outcomes has

value to both theory development and practice. Several ave-

nues for future work remain, and we hope that this study will

stimulate others to extend this line of research further.

Acknowledgments

The authors would like to thank Professor Deborah Compeau (senior

editor), the associate editor, and three anonymous reviewers for their

valuable comments and suggestions, which helped improve this

paper considerably.

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About the Authors

Sucheta Nadkarni is an associate professor of Management at the

University of Nebraska–Lincoln. She received her Ph.D. from the

University of Kansas. Her research focuses on cognitive issues in

strategic decision making and management information systems.

She has published or has papers forthcoming in journals such as

Strategic Management Journal , Journal of International Business

Studies, Organization Science, and MIS Quarterly.

Reetika Gupta is an assistant professor of Marketing at Lehigh

University. She received her Ph.D. from Baruch College, City

University of New York. Her research interests include consumer

behavior in interactive consumption environments and consumer

learning of new products.





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Tastee chocolate company

started a new plantTasteechocolate company opened its new plant in

California. This plant employs 1,000 full time

employees and is located Apple county.Tasteeinvesteda tfotal of 20 million dollars and the

company expects to produce over 200,000 units

of chocolate a month

More>>

Hurricanes hit some parts ofthe worldSome countries in South America were hit by some

powerful hurricanes which destroyed several

houses along the coastline. The local governmentdispatched approximately 2000 rescue workers to

affected areas and allocated 1 million dollars to

help in the reconstruction process.

More>>

Domestic unrest againstcurrent government in an Asian

countryThere were several demonstrations and strikes

against the local government in an Asian country.50 people were injured and no casualties were

reported. The government officials continued their

negotiations with the leaders of the demonstrating

organization to restore peace.

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Tastee chocolatecompany started a newplant

Tasteechocolatecompany openedits new plant

in Californi a. This plantemploys 1,000 full timeemployees and is locatedAppl ecounty.

Tasteeinvestedatfotalof 20milliondollars andthecompany expects toproduceover 200,000units ofchocolatea month

More>>

Hurr icanes hitsomeparts of the world

Somecountries inSouthAmericawerehit bysomepowerful hurricanes whichdestroyed

several houses alongthecoastline.Thelocalgovernmentdispatched approximately 2000

rescuew orkers toaffected areas andal located1milliondollars tohelpinthe reconstructionprocess.

More>>

Domestic unrest againstcurrent government in anAsiancountry

Therewere several demonstrations andstri kesagainst the local government in anAsi ancountry.50peoplewere injured andnocasualties were

reported. The governmentoffi cials continued their negotiations with theleaders of thedemonstrating

organizationtorestorepeace.More>>

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Tastee chocolate company

started a new plantTasteechocolate company opened its new plant in

California. This plant employs 1,000 full time

employees and is located Apple county.Tasteeinvesteda tfotal of 20 million dollars and the

company expects to produce over 200,000 units

of chocolate a month

More>>

Hurricanes hit some parts ofthe worldSome countries in South America were hit by some

powerful hurricanes which destroyed several

houses along the coastline. The local governmentdispatched approximately 2000 rescue workers to

affected areas and allocated 1 million dollars to

help in the reconstruction process.

More>>

Domestic unrest againstcurrent government in an Asian

countryThere were several demonstrations and strikes

against the local government in an Asian country.50 people were injured and no casualties were

reported. The government officials continued their

negotiations with the leaders of the demonstrating

organization to restore peace.

More>>

NEWS PAGE

NEWS SOURCES

Press

Television

Domestic

International

Home PageLow Coordinative

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A2. Illustration of Coordinative Website Complexity





Current bid US $139.45

Place Bid >

Time left: 22 hours 14 mins

1 - day l isting. Ends Dec - 0 805 16:52:10 PST

Start time: Dec - 07 - 05 16: 52:10 PST

History: 12 bids (US $0.01 start ing bid)

High Bidder: bistrobistro (128 )

Item l ocat ion: Ci nci nnati , Ohi oUnited States

Shipping, payment details and return policy

J O E ’ S G R E E N B A Y P A C K E R S ’

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Hyperlink: NFL ticket bid

Great Green BayPacker tickets ⎯

Chicago Bears@Green Bay PackersVenue:Lambeau Field

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Hyperlink: NFL ticket bid

Great Green BayPacker tickets ⎯

Chicago Bears@Green Bay PackersVenue:Lambeau Field

UncertainLink

Certain

Link

A3. Illustration of Dynamic Website Complexity

Ambiguous Hyperlinks for Shopping Cart Unambiguous Hyperlinks for Shopping Cart

misq2007 a task-based model of perceived website complexity

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