THE ROLE OF INTERACTIVITY AND INVOLVEMENT
IN ATTITUDE TOWARD THE WEB SITE
Jang-Sun Hwang, University of Tennessee, Knoxville, TN
Dr. Sally J. McMillan, University of Tennessee, Knoxville, TN
Attitude toward the Web site (AST) is an important measure of Web site effectiveness. While research
has been done to develop AST measures, more work is needed to identify specific predictors of AST. This
study identifies perceived interactivity and involvement with the subject of a site as possible predictors
and analyzes the impact of these two variables on AST. Both variables predict AST, but perceived
interactivity accounts for more of the variance in attitude than does involvement. Analysis of
relationships among the variables in this study suggests that the control sub-dimension of perceived
interactivity has the strongest correlation with attitude toward the Web site. Future studies need to
examine other predictors of AST using more respondents and different research environments.
Overview
What makes a Web site good? In recent years, Web sites have become a common marketing
communication tool, but both practitioners and academic researchers still struggle to measure the
effectiveness of these new tools. Researchers have suggested various methods for measuring Web site
effectiveness (e.g., Bruner II and Kumar 2000; Cho and Leckenby 1999; Pavlou and Stewart 2000;
Stevenson, Bruner II, and Kumar 2000). Many have suggested Web site effectiveness should be
measured with new methods that are different from traditional measures of advertising effectiveness.
Attitude toward the advertisement (AAD) is one of the most-frequently utilized measures of
effectiveness in the context of traditional media. Attitude toward the Web site (AST) is coming to be
recognized as an important measure of effectiveness of Web sites. As Chen and Wells (1999) argued,
though AST is similar in concept to AAD, new measures are needed to measure attitude toward this new
media environment. Rodgers and Thorson (2000) also proposed a new model explaining the structure of
interactive advertising. The antecedents of AST are much different from those of AAD. Thus, while we
can learn from the AAD literature, if we are to understand what makes Web sites effective, we must
begin by examining the factors affecting AST and the relationships among those factors.
This study focuses on possible predictors of AST beginning with a concept that is central to new
media: interactivity. In addition, the individual’s involvement with the topic of the Web site is also
examined as a possible predictor of AST. Inter-relationship among these explanatory factors is also
examined. Clearly, other factors may also affect AST, and AST alone does not fully measure Web site
effectiveness. Nevertheless, understanding attitude toward the site is an important first step in
understanding effectiveness of Web sites and, as indicated by the literature, interactivity and
involvement are two key measures that may help us to better understand AST.
Literature
Attitude toward the Web site
Traditionally, attitude toward the ad (AAD) as an affective response to ads has been a popular
indicator for measuring the effectiveness of advertising in traditional media contexts (for an excellent
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summary, See Brown and Staymen 1992). Most researchers examining AAD agreed, implicitly or
explicitly, on the importance of affective responses to the ad as an indicator of advertising effectiveness.
Attitude toward the Web site as a measure of the audience’s affective response has been
employed to assess the effectiveness of Web sites (e.g., Bruner II and Kumar 2000; Stevenson et al.
2000). Some researchers have borrowed from traditional measures of AAD to measure attitude toward the
Web site because of the absence of scales designed specifically to measure the effectiveness of Web
sites. However, Chen and Wells (1999) argued that a new scale is needed for this new medium and they
developed a scale to measure attitude toward the Web site (AST). The Chen and Wells scale was
developed primarily from the input of experienced Web users. Based on previous research on the
development of the AST scale, this measure seems to be a good starting point for understanding the
effectiveness of Web sites and is thus central to the current study.
But, simply measuring AST is not enough. Researchers must also explore what leads Web site
users to form attitudes. The level of interactivity users perceive Web sites to have may be an indicator
of attitude that is unique to new media environments. Traditionally, involvement has strong links to
attitude. Thus, involvement with the subject of the site might also influence attitude toward the site.
Interactivity
Much research on interactivity has focused on processes such as exchange of information
(Rafaeli 1988, 1990; Rafaeli and LaRose 1993; Zack 1993) or on functions such as chat rooms and
search engines that enhance interactivity (Ha and James 1998; Massey and Levy 1999; McMillan 1998;
Schultz 1999, 2000). A growing number of scholars focus on interactivity as a perceptual concept (Lee
2000; McMillan 2002; McMillan and Downes 2000; Wu 1999). Lee (2000) suggested that interactivity
should not be measured by counting features, but rather by investigating how users perceive and/or
experience those features. Whether they are examining processes, functions, or perceptions, most
scholars recognize one or more of three dimensions as being central to interactivity: direction of
communication, individual control, and time.
Researchers who examine ways that new media can facilitate interactions between humans often
focus on the importance of enabling two-way communication among individuals (Pavlik 1998; Rafaeli
and Sudweeks 1997; Zack 1993). Two-way communication is sometimes characterized as mutual
discourse (Burgoon et al. 2000; Hanssen, Jankowski, and Etienne 1996). Other scholars focus on the
capability of new media for providing feedback (Day 1998; Duncan and Moriarty 1998). While
direction of communication is central to human-to-human interaction, human-to-computer interaction
often centers on control (Huhtamo 1999; Preece 1993). Some studies focus more on how humans
control computers (Moon and Nass 1996; Murray 1997; Xie 2000) while other studies focus on how
control systems (such as navigation tools) are designed into new media environments (Laurel 1990;
Mahood, Kalyanaraman, and Sundar 2000; Nielsen 2000; Schneiderman 1998). A third type of
interactivity identified in the literature is interaction with the messages received both from other
individuals and from the computer. A key element of this interaction is time. Perception of interaction
with either human-based or computer-based messages is influenced by the speed with which messages
can be delivered and the speed with which individuals process messages (Nielsen 2000; Vora 1998).
Another time element important to interactivity is users’ ability to quickly navigate through a wealth of
information (Mahood, Kalyanaraman, and Sundar 2000; Wu 1999).
Each of the three dimensions identified above is central to the concept of interactivity, but in
much of the literature these concepts overlap. Communication and control overlap as higher levels of
control lead to more active participation in communication. As Naimark (1990) noted, interactivity is
often defined at the intersection of these two concepts. The intersection of time and communication is
often viewed in the framework of whether interactive communication occurs in real time or not (Kiousis
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1999; McGrath 1991; McMillan and Downes 2000). Time and control are also overlapping concepts as
the complexity of controls impacts on the time required to navigate through a new media environment
and the upon the individual’s engagement with the new media environment (Hoffman and Novak 1996;
Trevino and Webster 1992).
Researchers have begun to examine the relationship between interactivity and attitude toward the
Website. Some studies have found strong positive links between interactivity and attitude (Cho and
Leckenby 1999; McMillan 2000; Yoo and Stout 2001). However, Bezjian-Avery and her colleagues
(Bezjian-Avery, Calder, and Iacobucci 1998) found to their surprise that interactivity had no significant
relationship to attitude. New measures for both attitude and interactivity may be needed to clarify the
relationships between these two constructs in Web-based environments.
Involvement
Involvement scales have been widely used in studies that seek to understand how individual
differences affect responses to advertising messages. Zaichkowsky’s (1985) Personal Involvement
Inventory (PII) is one of the most popular scales for measuring context-free involvement. Many
subsequent studies employed some of the PII’s original 20 items (Celsi and Olson 1988; Maheswaran
and Meyers-Levy 1990; Mick 1992; Miller and Marks 1992).
Scholars have begun to explore relationships between individuals’ involvement with the topic of
a Web site and attitude toward that site. While some studies have found a strong link between
involvement and attitude (Cho and Leckenby 1999; McMillan 2000; Yoo and Stout 2001), others have
found ambiguous or insignificant relationships between these two concepts (Ahren, Stromer-Galley, and
Neuman 2000; Oginanova 1998). Thus, further research is needed to clarify both the concepts of
attitude and involvement and to fine tune measures for these concepts in a Web-based environment.
Research Questions
This study focuses on how perceived interactivity and involvement with the subject of a site
affect attitude toward the Web site (AST). While other factors may also predict AST, these two factors
offer an ideal starting point for understanding consumer attitudes toward Web sites. One of the key
variables (perceived interactivity) is unique to new media environments while the other (involvement
with the subject of the site) has been widely used in traditional media research. Thus, these two
concepts help us understand how attitude might be influenced both by unique characteristics of the Web
as well as by variation in individual characteristics such as involvement. Two key research questions
emerge from the literature: How well do perceived interactivity and involvement with the subject of a
site explain AST? What are the relationships among these predictors and AST?
Method
Data were collected with a Web-based self-administered survey. A total of 65 individuals from
various backgrounds evaluated one of the two experimental Web sites and completed a Web-based
survey. Many of the participants (56.9%) were non-students (e.g., business consultant, politician,
retired, etc). Age of the participants ranged from 21 to 48, and the gender distribution was almost even.
Data Collection
Before developing data collection tools, it was important to identify an appropriate topic for
exploration. A pre-test was conducted among 60 undergraduate students to determine which topics
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would be most likely to generate interactive behavior in terms of seeking information, communicating
with others via the Internet, and making an online purchase. The top five categories were computers,
music, books, clothing, and automobiles. Computers ranked highest for likelihood of seeking
information and communicating with others. Computers were the fourth-ranked product (after clothing,
music, and books) for likelihood of online purchase. The lower ranking on this measure was probably
because of the relative cost of computers. Based on overall assessment of the potential topics, the
researchers determined that computers were the best topic for this study.
Two Web sites were developed as an environment for evaluating individuals’ AST, perceptions of
interactivity, and involvement. Both Web sites were about notebook computers and contained virtually
identical information. However, one site was designed to have fewer interactive features and fewer
opportunities for interactive exchange thus making it likely to generate lower scores on a scale of
perceived interactivity than the other site that included features such as chat rooms, bulletin boards, a
site map, and enhanced navigation bars. The primary purpose for creating these two environments was
to generate variance in response to scale items designed to measure perceived interactivity.
Participants were randomly assigned to review either the high-interactivity or low-interactivity
Web site. Both Web sites included a menu item that led users to an online survey. Participants were
instructed to spend about 15 minutes reviewing a site before taking the survey. Participants were issued
a unique ID number that they provided when answering the survey. These numbers were used to track
which site participants had viewed as well as to ensure that each individual completed the survey only
once. Data collection tools (Web sites and survey) were pre-tested by eleven faculty members and eight
doctoral students. This pre-test led to some changes in both the Web sites and the survey.
Scales for Measuring Each Construct
Scales for AST and involvement were borrowed from previous literature, and the scale for
perceived interactivity was developed by a multi-stage process. All items of each construct were
measured using seven-point bipolar Likert scales. Three of the six items on the AST scale developed by
Chen and Wells (1999) were modified and employed. One item was the global evaluation of the Web
site: “compared to other Web sites I would rate this as … (one of the worst to one of the best). The
other two measures were: “I would like to visit this Web site again in the future,” and “I am satisfied
with the service provided by this Web site.” These three items provided a reliable measure of A ST
(Cronbach’s alpha = .7069). Three items were adapted from Zaichkowsky’s (1985) PII (Personal
Involvement Inventory) to measure involvement: “I have a great interest in notebook computers,”
“Notebook computers are very relevant to me,” and “I am highly involved in reading information about
notebook computers.” Respondents rated statements using a scale ranging from strongly disagree to
strongly agree. A high alpha coefficient (.8654) shows these items measure a single construct.
An 18-item scale was developed for measuring the three overlapping dimensions of perceived
interactivity identified in the literature: communication, control, and time. They were developed by
multi-stage procedures that began with an initial pool of items from three sources (literature, expert
interviews, and focus groups). Scale items were pre-tested and modified prior to two data collections
stages that were used to purify and assess the scale. Three factors of perceived interactivity emerged
from the scale-development process. The Real-Time Conversation sub-dimension combines elements of
two-way communication and time. The No Delay dimension describes the speed with which messages
are delivered. The Engaging sub-dimension is primarily about control but also includes time elements.
Table 1 shows factor loadings for the scale items. The three sub-scales had strong alpha coefficients
(.9034 for Real-Time Conversation, .9195 for No Delay, and .7889 for Engaging). Additionally, all 18
items were combined to create a single scale for perceived interactivity and that scale had an alpha
coefficient of .8816. This shows high reliability for all of the measures of perceived interactivity (MPI).
4
Table 1. Measures of Perceived Interactivity
Items
Real-Time
Conversation
.879
.833
.826
.807
.760
.758
.721
Enables two-way communication
Enables concurrent communication
-Non-concurrent communication
Is interactive
-Primarily one-way communication
Is interpersonal
Enables conversation
Loads fast
-Loads slow
Operates at high speed
Variety of content
Keeps my attention
Easy to find my way through the site
-Unmanageable
-Doesn’t keep my attention
-Passive
Immediate answers to questions
-Lacks content
No
Delay
Engaging
.936
.931
.924
.745
.745
.713
.640
.621
.597
.578
.534
Eigenvalue
6.205
2.635
1.975
% of Variance
34.471
14.640
10.970
- Phrase recoded for analysis
Extraction Method: Principal Component Analysis
Rotation Method: Direct Oblimin with Kaiser Normalization (Pattern Matrix)
Only loadings of .45 or higher are shown
Because measures of perceived interactivity are relatively new and because this construct
involves multiple overlapping concepts, it was important to cast a wide net in measuring perceived
interactivity. Thus, the combined scale composed of 18 items was used in this study. Respondents
indicated how well each of the 18 phrases described the Web site they had reviewed. Items were scored
on a seven-point scale ranging from “Not at all descriptive” to “Very descriptive.”
Results and Analysis
Multiple regression and correlation analysis were conducted to examine the research questions.
First, regression analysis employed the “forward” method to examine the explanatory power of two
independent variables, perceived interactivity and involvement, on attitude toward the Web site.
Regression analysis showed that perceived interactivity and involvement could explain variance in
attitude toward the Web site (AST) with statistical significance (p < .001). As shown in Table 2, the
model with these two independent variables yielded a relatively high F-ratio of 29.047 and 46.7% of
variance in AST was explained. Specifically, perceived interactivity was a more powerful predictor of
AST than was involvement with the subject of the site (t = 7.157 and t = 3.120, respectively). Research
question 1 asked, “How well do perceived interactivity and involvement with the subject of a Web site
5
explain the AST?” Findings reported in table 2, suggest that perceived interactivity and involvement play
a strong role in predicting attitude toward the Web site, but that other factors clearly play a role as well.
Table 2. Regression Model for Variables Predicting Attitude toward the Web site (AST)
SS
Regression
38.202
Residual
40.771
Total
78.973
R Square for the model = .467***
Variable
B
Interactivity
.845
Involvement
.249
Constant
-.851
**p<.01, *** p<.001.
df
2
62
64
SE B
.118
.080
.641
MS
19.101
.658
F
29.047***
Beta (standardized)
.655
.285
t
7.157***
3.120**
-1.328
Correlation was used to examine the relationships among variables: the combined MPI scale; the three
sub-dimensions of perceived interactivity (Real-Time Conversation, Engaging, and No Delay);
involvement; and AST. As detailed in Table 3, AST showed strong significant correlation with perceived
interactivity, but showed weaker correlation with involvement. Specifically, among the three subdimensions of perceived interactivity, Engaging had the highest correlation with A ST followed by RealTime Conversation and No Delay. Thus, the control aspect of perceived interactivity seems to a
particularly important measure of AST, but the difference between the 18-item MPI scale and the
Engaging sub-dimension of interactivity was minimal in terms of their correlation with AST.
Table 3. Correlations of Variables
Involvement (INV)
Interactivity (INT)
Real-Time Conversation (COM)
Engaging (ENGAGE)
No Delay (TIME)
*p<.05, **p<.01
AST
.239
.635**
.425**
.711**
.254*
INV
INT
COM
ENGAGE
-.071
-.184
.060
-.016
.825**
.819**
.608**
.460**
.282*
.350**
Among the three sub-dimensions of perceived interactivity, Real-Time Conversation and
Engaging were strongly correlated with each other and No Delay showed moderate correlation with the
other two dimensions. Time also showed moderate correlation with perceived interactivity as a whole,
while the other two dimensions had strong correlations with the overall MPI scale. These correlations
support the notion of overlapping concepts being central to the construct of perceived interactivity.
Research question 2 asked: “What are the relationships among these predictors and the AST?”
Correlation analysis suggests strong relationships among all of the sub-dimensions of perceived
interactivity and a strong relationship between perceived interactivity and attitude toward the site.
Weaker relationships were found between involvement and either AST or perceived interactivity.
Discussion
This study examined relationships between perceived interactivity, involvement with the topic of
a Web site, and attitude toward the Web site (AST). Specifically, it explored how well the first two
6
factors can explain AST. The study found that perceived interactivity and involvement can explain a
large portion of the variance in AST, but perceived interactivity is a stronger predictor than involvement.
One possible reason for the strong relationship between perceived interactivity and AST is that the study
operationalized both of these variables at the perceptual level. Thus, a strong positive reaction to the site
as a whole may be motivated, at least in part, by a strong sense that the user is able to interact with the
site. As shown in table 3, ability to control one’s viewing experiences is strongly related to perceived
interactivity as well as to AST.
As noted in the literature review, previous studies of relationships between attitude toward the
site and both interactivity and involvement have yielded differing and sometimes inconclusive results.
One of the reasons for these earlier findings might be that some studies had operationalized interactivity
in terms of features of the site that might enhance interactivity rather than in terms of users perception of
whether interactivity actually exists. Similarly, other studies have operationalized involvement in terms
of general involvement with technology rather than with involvement with the specific topic of the Web
site. When measures of attitude, perceived interactivity, and involvement are all considered at the
individual perceptual level, and are all are specific to the Web-based environment under study, more
direct relationships can be found.
By using both regression and correlation analysis, this study was able to further examine
relationships among the key dependent and independent variables. Interestingly, involvement did not
show any significant correlations with perceived interactivity factors, but it did significantly contribute
to the prediction of AST. Thus, the findings suggest that involvement and perceived interactivity operate
independently in affecting AST.
Because the study focused on only two major factors predicting AST, the proportion of variances
explained by the model is relatively small. Thus, any following study examining the factors affecting
AST needs to have more possible predictors in order to explain AST more accurately. Whenever possible,
future predictive variables should also be measured at the individual perceptual level to ensure that
whatever structural changes may have been made to a Web site are actually recognized by the user and
therefore play a real role in the development of attitude toward the Web site.
Additional methodological concerns represent minor limitations to this study. The small number
of independent variables may have negative impacts on the explanatory power of predictive model, but
with the current sample size of 65 no more than three predictors can be used with statistical certainty.
Given the small sample size and few independent variables, the fact that 45.6% of variances was
explained suggests the relative importance of both perceived interactivity and involvement in predicting
AST. Nevertheless, future studies should increase both the sample size and the number of predictors.
With a larger sample, future studies might also have respondents analyze “real” Web sites rather
than controlled experimental sites. With a wider sample of Web sites developed by different types of
organizations, more factors might be identified that impact on AST. Although this study controlled for
design factors, the experimental nature of the site may have created an artificial environment that could
be overcome by sampling from a larger number of sites.
Future studies should carefully operationalize each concept identified in this study and, with
larger samples, consider additional types of statistical analysis. For example, a curvilinear relationship
between the Engaging sub-dimension of perceived interactivity and attitude toward the Web site might
exist. Limitless options might overwhelm consumers and lead to negative attitudes toward sites. But
future studies should not focus exclusively on enhanced statistical techniques. In-depth qualitative work
is also needed to explore individuals’ reactions to Web sites and to better understand the attitude
formation process. Observation techniques, in-depth interviews, talk-along procedures, and focus
groups could all provide insight into how consumers develop attitudes toward Web sites.
Perceived interactivity and involvement with the topic of a Web site do affect attitude toward
that Web site. This study represents a first step in analysis of Web site effectiveness. People have more
7
favorable attitudes towards sites that they perceive to be interactive. Thus it is important to build Web
sites that will let individuals feel they are in control of their site visits and that also incorporate other key
elements of perceived interactivity: two-way communication and time sensitivity. People have more
favorable attitudes towards sites that provide content on topics with which they are involved. This
suggests the need for careful targeting of audiences for Web sites. It will do site developers little good
to attract unqualified visitors. Instead, marketing communication efforts need to be directed toward
identifying subject matter that will attract and retain a qualified and involved audience.
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