Applying the Rossiter-Percy Grid to Online Advertising Planning:
The Role of Product/Brand Type in Previsit Intentions
Guohua Wu
California State University, Fullerton
Abstract
This article examines the role of product/brand type on Web site previsit intentions by framing Web site visits according to the theory of planned behavior and applying the Rossiter-Percy grid to online advertising planning. The results show that Web site previsit intentions differ among the four different product types (low vs. high involvement × informational vs. transformational). Specifically, previsit intentions are higher for high involvement than for low involvement and for transformational than for informational products. When a Web site visit occurs in certain circumstances, previsit intentions relate positively to attitude toward the site, which partially mediates brand attitude changes for low involvement products and fully mediates them for high involvement products. The author discusses implications for online advertising planning and suggests some future research directions.
Introduction
In the age of the Internet, any marketer can set up a Web site to take advantage of its enormous potential for marketing products and services, provide customer support, or communicate with diverse stakeholders (Watson, Zinkhan, and Pitt 2000). One of the greatest benefits of the Internet involves harnessing its interactivity, which enables a user and a marketer to converse without losing mass marketing scale economies (Deighton 1996). For example, cluetrain.com predicts that “markets are conversations” on the Internet, and online marketers attempt to initiate conversations with their target audience in various ways, ranging from static banners to animated banners to interstitials to pop-ups to pop-unders to the recently invented video overlay ads.
Such a variety of online advertising formats reflects online marketers’ consistent efforts to test online consumers’ levels of tolerance for commercial interruptions, which characterizes the old one-way broadcast marketing paradigm. However, in the new, two-way interactive marketing paradigm, the power balance between consumers and marketers has tipped in favor of consumers, who have more and faster access to information than ever before (Urban 2005) and exert more control over information flow (Ariely 2000). Consequently, their tolerance for commercial interruptions is low, as evidenced by the popular use of pop-up blockers. But that intolerance does not mean they do not want any conversations with marketers. Rather, they prefer to initiate the conversation and want to be entertained and engaged on their own terms. The huge success of search engine marketing by Google demonstrates online consumers’ efforts to seek out information from and interactions with online marketers.
When an online user must deal with an intrusive invitation to a site visit (e.g., pop-up or pop-under), he or she likely determines whether to click through within split seconds. For example, a full-screen interstitial promoting a detergent brand (e.g., Tide) may prompt a user to dismiss it quickly as annoying, but if another full-screen interstitial promotes a cruise brand (e.g., Carnival), he or she may wait patiently for it to display or click on to the target site. The phenomenon exemplified in these examples presents some interesting research questions: (1) Does product type play an important role in influencing online consumers’ previsit (i.e., pre-exposure) intentions? and (2) Do previsit intentions influence subsequent evaluations in terms of attitude toward the site and attitude toward the brand if the visit occurs in certain circumstances? This article empirically addresses these two questions by framing Web site visits within the theory of planned behavior (Ajzen 1991) and applying the Rossiter-Percy (1987) grid to online advertising planning.
Background
A site previsit is analogous to a “pre-exposure” in the literature. Intention captures the motivational factors that affect behavior and indicates how hard a person is willing to try or how much effort he or she will exert to perform the behavior (Ajzen 1991). Therefore, site previsit intentions are similar to the concept of “pre-exposure motivation” (Putrevu and Lord 2003), defined as an online consumer’s likelihood of visiting a Web site voluntarily or under volitional control. These intentions thus reflect the notion of an online consumer’ readiness, interest, or desire to interact with a Web site (MacInnis, Moorman, and Jaworski 1991) or the degree to which she or he willingly seeks out desired and deliberate exposure (Cho 1999; Raman and Leckenby 1998) prior to the actual visit. Previsit intentions emphasize an important aspect of the “under volitional control” concept, meaning that an online consumer can decide at will to visit or not visit a Web site. Any activities or factors that inhibit this exercise will likely decrease previsit intentions. For example, pop-up ads that disrupt the flow state in which online consumers are engrossed during a task (Hoffman and Novak 1996) may drastically reduce their previsit intentions, which in turn may create a feeling of reactance (Brehm and Brehm 1981).
By definition, previsit intentions encapsulate the motivational forces that exist prior to a visit. For example, when prompted by a pop-up ad for Dove, an online consumer must decide quickly whether to visit the target site (www.campaignforrealbeauty.com), and the willingness to visit at that very moment constitutes his or her previsit intention, which is determined by several factors, such as the characteristics of the pop-up ad (e.g., interest, hedonism, utilitarianism; Olney, Holbrook, and Batra 1991), the product type or category (i.e., soap), media publicity, or word of mouth about the brand. In this study, I examine product type empirically as one of the factors driving previsit intentions.
Consumer researchers recognize the effect of product or brand type on consumer purchasing and consumption behavior and have identified various ways to classify products according to the situations in which the product, person, and environment interact (Fennell 1978; Hirschman and Holbrook 1982; Park, Jaworski, and MacInnis 1986; Rossiter and Percy 1987; Vaugh 1980, 1986). The literature reveals two major ways to categorize products: (1) on one dimension, such as hedonic versus utilitarian, or (2) on two dimensions, as in the FCB grid (Vaugh 1980, 1986) (e.g., high vs. low involvement × think vs. feel) or the Rossiter-Percy (1987) grid (e.g., high vs. low involvement × informational vs. transformational). I choose Rossiter and Percy’s (1987) two-dimensional conceptualization of product or brand type primarily because of its conceptual rigor and operational usefulness for practical applications, as supported by Rossiter, Percy, and Donovan (1991). The first dimension, involvement, is purely dichotomous and defined as “perceived risk.” High involvement means that a particular audience perceives the brand choice as risky enough to deserve deep-level information processing, whereas low involvement means that the audience regards the perceived risk in the brand choice decision as low enough to try it first. The second dimension of product type pertains to the purchase motive, whether informational or transformational. Informational motives are negatively reinforcing and can be satisfied by providing information about the product or brand, with a corresponding emotional state. They include problem removal (from anger to relief), problem avoidance (from fear to relaxation), incomplete satisfaction (from disappointment to optimism), mixed approach avoidance (from guilt to peace of mind), and normal depletion (from mild annoyance to convenience). In contrast, transformational motives are positively reinforcing and promise to enhance the user by bringing about a transformation in the brand user’s sensory, mental, or social state. They include sensory gratification (from dull to elated), intellectual stimulation (from bored to excited), and social approval (from apprehensive to flattered). Along these two dimensions, the Rossiter-Percy (1987) grid identifies four types of products/brands: (1) low involvement and informational (e.g., aspirin), (2) low involvement and transformational (e.g., candy), (3) high involvement and informational (e.g., insurance), and (4) high involvement and transformational (e.g., cars). Applying this classification of products or brands to the Web produces a corresponding categorization of four types of product or brand Web sites, each of which resides in one of the four quadrants.
FIGURE 1. A Conceptual Framework for Planned Web Site Visit
Theory of Planned Web Site Visit
Figure 1 provides a conceptual framework for the current study, based on Ajzen’s (1991) theory of planned behavior. Attitude toward behavior refers to the degree to which a person has a favorable or unfavorable evaluation of the behavior in question. The evaluation is based on beliefs about the attitude object, each of which links behavior to certain positively or negatively valued attributes. Likewise, online consumers may hold positive or negative attitudes toward visiting a specific product or brand type Web site because they associate the site visit with positive or negative expenditures of time and effort. For example, most online consumers consider visiting a detergent site such as tide.com a waste of attention and time but regard visiting royalcarribean.com as worthwhile.
Subjective norms refer to the social pressure to perform or not perform a particular behavior. In this context, online consumers’ subjective norm regarding visiting a specific type of product Web site reflects what important others think of that visit. To illustrate, someone who visits tide.com might be thought of as unimaginative or boring, whereas someone who visits royalcarribean.com might be regarded as fun or exciting.
Perceived behavioral control refers to the ease or difficulty of performing or not performing the behavior of interest. With regard to visiting a Web site, it is easy and simple for online consumers to decide whether to visit a particular Web site on the basis of an assessment of the opportunities and resources available to them, such as time, energy, skills, and timing. But they also may perceive a loss of behavioral control because of the presence of intrusive online ads, such as pop-ups, pop-unders, and interstitials. In particular, they may react very negatively when they encounter intrusive ads that promote routinely purchased products that are of low importance.
In response to this discussion about the role of product type in attitude toward visiting Web sites and the effects of subjective norms and perceived behavioral control on previsit intentions, the following hypothesis emerges:
H1a: Previsit intentions differ among the four product types (high vs. low involvement × informational vs.transformational).
Yoon and Kim (2001), in their attempt to relate salient product characteristics to media choice, use four product types: automobiles, luxury watches, shampoos, and fast food. They find that the Internet is more suited for high-involvement product types, such as automobiles and luxury watches, than for low-involvement products such as shampoos and fast food. From a cost-benefit analysis perspective (Stigler 1961), because consumers search until their perceived marginal costs of search equal the perceived marginal benefits, it makes sense that online consumers will spend their valuable time and cognitive energy on product or brand choice situations that bear higher perceived risks. Thus, online consumers are more likely pay voluntary visits to high-involvement Web sites than to low-involvement Web sites.
H1b: Previsit intentions are higher for a high-involvement product type than for a low-involvement product type.
In the same study, Yoon and Kim (2001) find that the Internet is more suited for “thinking” products, such as automobiles and shampoos, than for “feeling” products, such as fast food and luxury watches. Similarly, Dahlen and Bergendahl (2001) indicate that click-through rates are higher for functional products than for expressive products, which suggests online consumers are more likely to search for information about functional, thinking, or informational products than for information about expressive, feeling, or transformational products. Hence,
H1c: Previsit intentions are higher for an informational product than for a transformational product type.
The proposed framework for planned Web site visits predicts that attitude toward visiting certain types of product or brand Web sites influences site previsit intentions, which in turn affects attitude toward the site and attitude toward the brand if the site visit occurs in certain circumstances. However, it remains unclear how previsit intentions influence attitude toward the site. Existing literature suggests two factors define a clear relationship between previsit intentions and attitude toward the site when previsit intentions are low. First, online consumers tend to have less tolerance for commercial interruptions and expect to be left alone, because they are focused on goal-directed or experiential-directed flow activities (Novak, Hoffman, and Duhachek 2003). The phenomena of so-called “banner blindness” (Benway 1999) and ad avoidance (Cho and Cheon, 2004) on the Internet confirm that online consumers concentrate so much on their tasks that anything irrelevant to the task at hand gets selectively filtered. Second, when online consumers must deal with intrusive online marketing tactics, such as pop-up or rich media ads, they likely perceive a loss of control or an intrusion on the freedom of their private space online, which prompts a feeling of “reactance” (Brehm and Brehm 1981) that results in negative consequences for marketers (Edwards, Li, and Lee 2002). Empowered online consumers demand that marketers treat them as equal partners in a consumer-marketer relationship. Hoffman, Novak, and Peralta (1999) argue that online marketers should develop profitable exchange relationships with online consumers by allowing the balance of power to shift toward more cooperative consumer-marketer interactions, which assumes that both consumers and marketers are willing and ready to interact. As a result, online consumers who are less willing to interact voluntarily with online marketers might react more negatively and hold a more unfavorable attitude toward the intended target Web site and the advertised brand than those who are more willing or ready to do so.
However, when someone has high previsit intentions, he or she also may have higher expectations and be more disappointed if the visit experience falls short of his or her expectations; in turn, his or her attitude toward the site might be more negative as well. Thus,
H2a: If the site visit occurs in certain circumstances, an online consumer displaying either low or high previsit intentions has a more negative attitude toward the site than an online consumer displaying medium previsit intentions.
On the basis of the well-established mediating role of ad attitude in brand attitude in traditional consumer research (MacKenzie and Lutz 1989; MacKenzie, Lutz, and Belch 1986) and the validation and extension of MacKenzie, Lutz, and Belch’s (1986) dual mediation model to the Internet context (Karson and Fisher 2005a, b),
H2b: Attitude toward the Web site (b) positively influences brand attitude change and (c) mediates the influence of previsit intentions on brand attitude change.
Method
In a pretest conducted to determine which stimulus Web sites to use for the main study, 34 students in a consumer behavior class at a large public university in the United States considered the Rossiter-Percy grid. The students received a class assignment with the following instructions:
Please list the first three brands that come to your mind for each product category shown in each quadrant of the Rossiter-Percy grid.
From the assignments turned in, I counted the mentions of each brand. The brand that received the highest number of mentions in each quadrant is selected, namely, Bayer aspirin from the low involvement, informational quadrant; Snickers candy bar in the low involvement, transformational quadrant; Ford cars in the high involvement, transformational quadrant; and State Farm insurance in the high involvement, informational quadrant.
One hundred fifteen students from the same university volunteered to participate in the main study, which took place in a computer lab, for extra credit. The participants’ average age was 22 years. Of the 115 participants, 37.4% (43) are men. Approximately 73% work either part-time or full-time and work an average of 22 hours per week. More than 90% report that they felt comfortable or very comfortable performing computer-related tasks, such as browsing the Web and shopping on the Internet. They spend an average of 11.8 hours per week on the Web. Fifty-seven percent said they have high-speed internet access (DSL or cable modem) at home.
Procedure
A PowerPoint presentation guided the participants in browsing the four Web sites. To minimize order effects, participants were randomly assigned to four different order sequences for the site browsing task. The participants received extra credit as an incentive for them to visit these Web sites, regardless of their previsit intention levels. This procedure simulates a real-world situation, in which online marketers often offer incentives to lure online consumers to a target site. The participants were told that the purpose of the study was to help understand how consumers processed information online. After a Web site address appeared on the large screen, controlled from a LCD projector, the participants typed the Web site address into an Internet Explorer browser and began browsing for approximately 4 minutes. Then they stopped to fill out a paper-and-pencil questionnaire. This process repeated for the other three Web sites. Finally, they completed the demographics and Web usage portions of the questionnaire. Finally, they were debriefed, thanked, and dismissed. Each session lasted 30 minutes. Over two weeks, 22 sessions took place, and 115 completed survey questionnaires were collected.
Measure
The study manipulates product type as a within-subjects variable, because each participant browsed all four Web sites. The measure of previsit intention uses the statement: “How likely would you be to visit this Web site if NOT for participating in the research study?” Participants responded on a five-point Likert-type scale, anchored at 1 “extremely unlikely” and 5 “extremely likely.” The measure of brand attitude change employs the statement: “In which way did your visit to this Web site today affect your perception of the advertised brand?” (five-point Likert-type scale, 1 “very negatively” and 5 “very positively”). This newly developed measure captures the net impact of a site visit experience on brand attitude. Because these measures are very straightforward and concrete, single-item measures are adequate (Rossiter 2002). The attitude toward the Web site measure relies on Chen and Wells’s (1999) attitude toward the site scale (α = .87 bayer.com; α = .92 snickers.com; α = .89 statefarm.com; α = .85 ford.com).
Results
The descriptive statistics in Table 1 refer to the three key variables in this study: previsit intention, attitude toward the site, and brand attitude change. The test of H1a uses a general linear model (GLM) repeated-measures procedure. According to H1a, previsit intentions differ among the four types of products; the significant results (F(3, 104) = 48.25, p < .001) support this claim. Participants are most likely to visit ford.com (high involvement, transformational, mean [M] = 3.19, standard deviation [SD] = 1.26) and least likely to visit bayer.com (low involvement, informational, M = 1.62, SD = .91). However, the post-hoc analysis shows no differences between snickers.com (low involvement, transformational, M = 2.58, SD = 1.41) and statefarm.com (high involvement, informational, M = 2.46, SD= 1.25) in terms of previsit intentions.
A two-way ANOVA with repeated measures serves to test H1b and H1c. It indicates no signification interaction and two main effects. In support of H1b, which predicts previsit intentions are higher for high- than for low-involvement products, a main effect of involvement emerges (= 2.82, standard error [SE] = .08; Mlow involvement = 2.09, SE = .10, F(1,106) = 44.77, p < .0001). H1c predicts higher previsit intentions for informational than for transformational products. A main effect of motive emerges (Minformational = 2.02, SE = .08; Mtransformational = 2.89, SE = .10, F(1,106) = 60.06, p < .0001), but the direction is opposite to that hypothesized, in conflict with H1c. I pose three possible explanations. First, as the Web matures, consumers may perceive it as a multimedia platform that can be more affectively involving for transformational products. Second, the participants are younger and thus may be more comfortable using the Web to satisfy their hedonic needs, such as sensory gratification from transformational products. Third, the stimulus Web sites refer to well-known brands, and as Dahlen (2002) notes, functional products have higher initial click-through rates that quickly deteriorate with repeated exposures, whereas expressive products enjoy increasing click-through rates with repeated exposures. Therefore, online consumers may be more likely to click on transformational products than informational ones if the products or brands are already familiar to them.
TABLE 1. Descriptive Statistics for the Three Key Variables in the Study
To test H2a, the data collected from the four different stimulus Web sites join to form a data set. Previsit intention therefore gets recoded into a three-group categorical variable (i.e., low, medium, and high). The results from an ANOVA analysis show that the three groups differ significantly on attitude toward the site (Mlow = 2.71, SD = .83; Mmedium = 3.58, SD = .70; Mhigh = 4.23, SD = .62, F(431, 2) = 167.33, p < .001), but previsit intention has a consistent positive effect on attitude toward the site across the three groups. Accordingly, H2a receives only partial support, because the data do not suggest that high previsit intention leads to higher expectations, which causes a poor attitude toward the site.
The tests of H2b and H2c employ a mediational analysis, using Baron and Kenny’s (1986) framework for the four sites. The bayer.com data set encounters three regression models. The first regression uses previsit intention as the independent variable and attitude toward the site (Asite) as the dependent variable. The effect of the independent variable significantly explains the variance in the hypothesized Asite mediator (β = .50, p < .0001), which suggests that Asite relates to previsit intention, whose effect it supposedly mediates. The second regression model, with brand attitude change as the dependent variable and the mediator (Asite) as the independent variable, indicates that Asite significantly accounts for the variance in brand attitude change (β = .64, p < .0001). Finally, the third model contains brand attitude change as the dependent variable and previsit intention and attitude toward the site as independent variables. The effect of previsit intention is not significant (β = .59, p = .56) after controlling for the significant effect of attitude toward the site (β = .62, p < .001). Similar mediational analyses for the snickers.com, statefarm.com, and ford.com data sets produce the results featured in Table 2, which all support H2b and H2c. That is, previsit intentions have a significant impact on attitude toward the site, which in turn positively influences brand attitude change across four different product Web sites. However, regarding the mediating role of attitude toward the site on brand attitude change, full mediational effects emerge for low-involvement product types (bayer.com and snickers.com), but only partial mediation appears to exist for high-involvement product types (Baron and Kenny 1986). Therefore, H2c is partially supported.
TABLE 2. The Mediating Effect of Attitude toward Site on Brand Attitude Change
Discussion
This study applies the Rossiter-Percy grid to online advertising planning by empirically investigating the differential levels of Web site previsit intentions for four different product types (high vs. low involvement × informational vs. transformational) and the subsequent impact on advertising effectiveness measures, such as attitude toward the site and brand attitude change. The results of this study thus have important implications for online advertising planning.
The notion that product type influences previsit intentions is intuitive and obvious, but little empirical research actually examines this relationship. For example, Putrevu and Lord (2003) develop four sets of propositions for the four different product types in the FCB grid but just assume a relationship between product type and pre-exposure motivation. In contrast, this study explicitly tests the relationship between product type and previsit intentions. In addition, this study reveals that previsit intention has both direct and indirect effects (via attitude toward the site) on brand attitude change toward high-involvement products but only indirect effects (via attitude toward the site) on brand attitude change toward low-involvement products. This finding appears consistent with the elaboration likelihood model predictions (Petty and Caccioppo 1986). When online consumers display low previsit intentions for low-involvement products, their limited attention focuses on peripheral, non-product features and feelings contained in the Web site, their brand information processing is low, and the effect of previsit intentions on brand attitude change operates through a peripheral route to persuasion. In contrast, when online consumers display high previsit intentions for high-involvement products, their strong attention focuses on central, product-related features and factual information, their brand information processing is high, and their brand attitude change goes through a central route to persuasion.
Practical Implications for Online Advertising Planning
Although this study only tests one product Web site in each of the four quadrants in the Rossiter-Percy grid, it still provides some initial empirical evidence in support of the usefulness of the grid for online advertising planning. By recognizing the differential levels of previsit intentions that online consumers display for different product types and their potential impacts on attitude toward the target site and brand attitude change, online marketers can adopt the offline advertising tactics discussed by Rossiter, Percy, and Donovan (1991) to develop their online advertising objectives and strategies. For example, Rossiter, Percy, and Donovan (1991) recommend that marketers in the low involvement, informational quadrant should adopt a simple problem-solution format in an emotional portrayal of extreme benefit claim(s), though it is not necessary for consumers to like the ad. In online terms, this recommendation implies that intrusive advertising may be used to boost low previsit intentions. But because online consumers have a lower tolerance for such interruptions, they react more negatively to the ad and ultimately to the brand. The evidence from this study pertaining to bayer.com suggests that it is not easy to overcome the effects of low previsit intentions on attitude toward the site or brand attitude change. Perhaps the participants believed their visit experience not worthwhile or had regrets about the visit. Thus, suggestive or seductive tactics should attempt to lure, rather than drive, them to a Web site visit. Original and entertaining ads or ads that offer incentives, such as a chance to win something in a sweepstakes or accumulate reward points, may compel them to visit voluntarily. Again, it remains of paramount importance that online marketers in the low involvement, informational quadrant respect online consumers by minimizing their use of intrusive tactics and maximizing the power of creativity in ads and Web sites.
For products in the high involvement, transformational quadrant, Rossiter, Percy, and Donovan (1991) recommend enabling the target audience to identify personally with the emotions and lifestyles associated with product use, as portrayed in the ad. The evidence in relation to the ford.com data set proves this point; the participants showed the highest previsit intention, highest attitude toward the site, and most positive brand attitude change. High previsit intentions for this product type clearly have overwhelmed any negative perceptions associated with forced exposure. Thus, marketers of such products and services (e.g., vacations, fashion clothing) should employ a wide range of online advertising tactics to drive, seduce, or lure visitors to experience their consumption situations in the real world.
For products in the low involvement, transformational quadrant, Rossiter, Percy, and Donovan (1991) recommend that the target audience should be able to see that the emotion portrayed in the ad is unique and authentic, and they must like the ad. The evidence from the current study for snickers.com shows that the participants’ previsit intentions are somewhat below neutral point, but their attitude toward the site is a little higher than neutral; in particular, their brand attitude change is positive. These findings suggest that the participants must have had a compelling experience on snickers.com. Online marketers in this quadrant should use an affect-laden approach to engage online consumers, and intrusive tactics online are acceptable only if the emotional execution of the ads and Web sites is genuine and true. For example, Dove’s campaign for real beauty (www.campaignforrealbeauty.com) connects with many women on a personal and emotional level, elevating their previsit intentions for the soap brand.
Finally, for products in the high involvement, informational quadrant, the target audience must believe the benefit claims because of an emotional portrayal, but they do not have to like the ad (Rossiter, Percy, and Donovan 1991). Evidence from this study indicates that the participants’ previsit intentions move below neutral point, but they hold a somewhat positive attitude toward the site and somewhat positive brand attitude change. Therefore, participants apparently did not react as negatively to the prompted visits and went along to maximize their information benefits. Marketers of informational, high-involvement products (e.g., insurance, home renovation) thus might use limited intrusive advertising and marketing communication tactics, at least moderately, to entice visits to their Web sites.
In summary, the old broadcast model of driving consumers to Web sites may not be equally effective for some product types and even be ineffective in others (e.g., low involvement, informational quadrant). In online environments, marketers should try harder to take a soft sell approach by wooing consumers into a conversation. If markets are conversations, as heralded by cluetrain.com, conversations are the bridges to mutually beneficial relationships between brands and consumers-exactly what advertising and marketing communications are all about.
Limitation and Future Research Directions
The current study uses the theory of planned behavior as a conceptual foundation and applies the Rossiter-Percy grid to online advertising planning. Some might question the link between pre-exposure or previsit intentions and post-exposure attitude toward the site, because once the visit takes place, previsit intentions are no longer relevant. Although this study relies on the theory of planned behavior, the exact mechanism by which previsit intentions influence attitude toward the site remains to be explored. For example, perhaps online consumers can experience regret and dissatisfaction, which would affect their site attitude negatively. Further research should investigate whether regret plays a mediating role in previsit intention’s impact on attitude toward the site and attitude toward brand. In addition, product or brand type is only one of the possible antecedents of previsit intentions; other factors such as the creative aspect of an online ad also should be investigated.
Three methodological weaknesses also mark this study. First, it tests only one product Web site from each of the four quadrants in the Rossiter-Percy grid. Second, the student participants may find some of the stimulus product Web sites less directly relevant. Third, this study took place in a laboratory setting and used extra credit as incentives to entice the participants to visit. Additional studies could adopt a field experimental approach, use real consumers and real incentives, and include more Web sites from each quadrant of the Rossiter-Percy grid.
References
Ajzen, Icek (1991), “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes, 50, 179-211.
Ariely, Dan (2000), “Controlling the Information Flow: Effects on Consumers’ Decision-Making and Preferences,” Journal of Consumer Research, 27 (September), 233-48.
Baron, Reuben M. and David A. Kenny (1986), “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations,” Journal of Personality and Social Psychology, 51 (December), 1173-82.
Benway, Jan P. (1999), “Banner Blindness: What Searching Users Notice and Do Not Notice on the World Wide Web,” Doctoral dissertation, Rice University.
Brehm, Sharon S. and Jack W. Brehm (1981), Psychological Reactance: A Theory of Freedom and Control, New York: Academic Press.
Chen, Qimei and William Wells (1999), “Attitude toward the Site,” Journal of Advertising Research, 39 (September/October), 27-37.
Cho, Chang-Hoan (1999), “How Advertising Works on the WWW: Modified Elaboration Likelihood Model,” Journal of Current Issues & Research in Advertising, 21 (Spring), 33-50.
— and Hongsik John Cheon (2004), “Why Do People Avoid Advertising on the Internet?” Journal of Advertising, 33 (Winter), 89-97.
Dahlen, Micael (2002), “Thinking and Feeling on the World Wide Web: The Impact of Product Type and Time on World Wide Web Advertising Effectiveness,” Journal of Marketing Communications, 8, 115-125.
— and Jonas Bergendahl (2001), “Informing and Transforming on the Web: An Empirical Study of Response to Banner Ads for Functional and Expressive Products,” International Journal of Advertising, 20, 189-205.
Deighton, John (1996), “The Future of Interactive Marketing-Introduction,” Harvard Business Review, 6, 151-60.
Edwards, Steven M., Hairong Li, and Joo-Hyun Lee (2002), “Forced Exposure and Psychological Reactance: Antecedents and Consequences of the Perceived Intrusiveness of Pop-Up Ads,” Journal of Advertising, 31 (Fall), 83-95.
Fennell, Geraldine (1978), “Consumer’s Perceptions of the Product-Use Situation,” Journal of Marketing, 42 (April), 38-47.
Hirschman, Elizabeth C. and Morris B. Holbrook (1982), “Hedonic Consumption: Emerging Concepts, Methods and Propositions,” Journal of Marketing, 46 (Summer), 92-101.
Hoffman, Donna L. and Thomas P. Novak (1996), “Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations,” Journal of Marketing, 60 (Fall), 50-68.
—, —, and Marcos A. Peralta (1999), “Information Privacy in the Marketplace: Implications for the Commercial Use of Anonymity on the Web,” Information Society, 15 (April-June), 129 – 39.
Karson, Eric J. and Robert J. Fisher (2005a), “Predicting Intentions to Return to the Web Site: Extending the Dual Mediation Hypothesis,” Journal of Interactive Marketing, 19 (summer), 2-14.
— and — (2005b), “Reexamining and Extending the Dual Mediation Hypothesis in an Online Advertising Context,” Psychology and Marketing, 22 (April), 333-51.
MacInnis, Deborah J., Christine Moorman, and Bernard J. Jaworski (1991), “Enhancing and Measuring Consumers’ Motivation, Opportunity,” Journal of Marketing, 55 (October), 32-53.
MacKenzie, Scott B. and Richard J. Lutz (1989), “An Empirical Examination of the Structural Antecedents of Attitude toward the Ad in an Advertising Pretesting Context,” Journal of Marketing, 53 (April), 48-65.
—, —, and George E. Belch (1986), “The Role of Attitude toward the Ad as a Mediator of Advertising Effectiveness: A Test of Competing Explanations,” Journal of Marketing Research, 23 (May), 130-143.
Novak, Thomas P., Donna L. Hoffman, and Adam Duhachek (2003), “The Influence of Global-Directed and Experiential Activities on Online Flow Experiences,” Journal of Consumer Psychology, 13 (1-2), 3-16.
Olney, Thomas J., Morris B. Holbrook, and Rajeev Batra (1991), “Consumer Responses to Advertising: The Effects of Ad Content, Emotions, and Attitude toward the Ad on Viewing Time,” Journal of Consumer Research, 17 (March), 440-53.
Park, C. Whan, Bernard J. Jaworski, and Deborah J. MacInnis (1986), “Strategic Brand Concept-Image Management,” Journal of Marketing, 50 (October), 135-45.
Petty, Richard E. and John T. Cacioppo (1986), Communication and Persuasion: Central and Peripheral Routes to Attitude Change, New York: Springer-Verlag.
Putrevu, Sanjay and Kenneth R. Lord (2003), “Processing Internet Communications: A Motivation, Opportunity and Ability Framework,” Journal of Current Issues and Research in Advertising, 25 (Spring), 45-59.
Raman, Niranjan V. and John D. Leckenby (1998), “Factors Affecting Consumers’ ‘Webad’ Visits,” European Journal of Marketing, 32, 737-48.
Rossiter, John R. (2002), “The C-OAR-SE Procedure for Scale Development Marketing,” International Journal of Research in Marketing, 19, 305-335.
— and Larry Percy (1987), Advertising and Promotion Management, Boston, MA: McGraw-Hill.
—, —, and Robert J. Donovan (1991), “A Better Advertising Planning Grid,” Journal of Advertising Research, 31 (October/November), 11-21.
Stigler, George J. (1961), “The Economics of Information,” Journal of Political Economics, 29, 213-225.
Urban, Glen L. (2005), “Customer Advocacy: A New Era in Marketing?” Journal of Public Policy & Marketing, 24 (1), 155-59.
Vaugh, Richard (1980), “How Advertising Works: A Planning Model,” Journal of Advertising Research, 20 (September/October), 27-30.
— (1986), “How Advertising Works: A Planning Model Revisited,” Journal of Advertising Research, 26 (January/February), 27-30.
Watson, Richard T., George M. Zinkhan, and Leyland F. Pitt (2000), “Integrated Internet Marketing,” Communications of the ACM, 43 (June), 97-102.
Yoon, Sung-Joon and Joo-ho Kim (2001), “Is the Internet More Effective than Traditional Media? Factors Affecting the Choice of Media,” Journal of Advertising Research, 41 (November/December), 53-60.
About the Author
Guohua Wu (Ph.D., University of Texas at Austin) is an Assistant Professor of Communications at California State University, Fullerton. His research interests focus on examining the relationships among users’ perceived interactivity in a computer-mediated environment, attitude and trust formation. His articles have appeared in International Journal of Advertising, Journal of Consumer Marketing, Journal of Interactive Advertising, Journal of Current Issues and Research in Advertising, and Journal of Computer-mediated Communication.
Note: The author gratefully acknowledges Yuping Liu’s valuable insights on the earlier version of this article.
