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.
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.
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 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.
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 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 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.
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?
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.
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 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 AST (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).
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 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)
R Square for the model = .467***
**p<.01, *** p<.001.
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 sub-dimensions of perceived interactivity, Engaging had the highest correlation with AST followed by Real-Time 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
Real-Time Conversation (COM)
No Delay (TIME)
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.
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 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.
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 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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