Journal of Interactive Advertising, Volume 3, Number 1, Fall 2002
This article focuses on how Internet users change their behaviors and responses to marketing with increasing experience. Data from three empirical studies show several effects of increasing Internet user experience. Experienced users have shorter and more focused Web sessions, and they tend to reduce their visits to a smaller number of well-known Web sites. Furthermore, as experience increases, Internet users screen out advertising and they are less inclined to click on banner ads and less likely to be affected by ad exposures. The article tries to explain these effects, thus advancing theory on Internet marketing. Important implications for Web marketing practice are provided.
The Internet is the fastest growing medium of all times (Eighmey and McCord 1998). As consumers move online, so do advertisers. Internet advertising spending is increasing at a record pace, continuously taking on a larger share of the advertising budgets, according to the Interactive Advertising Bureau (IAB 2001). Marketers spend more and more money trying to influence consumers on the Web, especially through banner advertising (IAB 2001). Is this really a good idea?
As the Web medium matures, so do the users. Literature on experience suggests that consumers react differently to marketing and may be harder to influence as they become more experienced (cf., Alba and Hutchinson 1987; Maheswaran and Sternthal 1990; Haider and Frensch 1999). In this article we will investigate a number of effects on user behavior that come from increasing Internet experience. We will also examine how experience affects consumer response to Web advertising.
The research on Internet user experience to date is scarce, specifically concerning the effects of incremental experience over time. A few authors have noted that experienced users seem to differ in their cognition and behaviors from less experienced users. Ward and Lee (2000) find that experienced users rely less on brand names. Bruner and Kumar (2000) conclude that these users are less irritated by "noisy" Web page layouts. Novak, Hoffman, and Yung (2000) find that experienced users experience flow in a different way from novice users. However, the reasons for these effects are not given much elaboration. In this article we investigate some of the mechanisms that are at work when Internet users become more experienced. Based on this, we come one step closer to predicting and explaining changes in user behavior. The article adds new insights to theory on Internet usage by looking at previously uninvestigated relationships, and provides important implications for Web marketing practice. Data from three different empirical studies of Swedish Internet users are analyzed.
There are several factors at play when experienced Internet users change their behaviors. Next, we will review literature on consumer experience and identify three major effects that should influence Internet user behavior. Thereafter, we will look into theory on automaticity (i.e., the development of automatic response behaviors) and changing advertising context effects (i.e., involvement and pleasure), both consequences of experience, and their effects on Internet user susceptibility to Web advertising. The developed hypotheses are then tested empirically.
With repeated usage of a product or performance of a task, people become more experienced (Bettman and Park 1980; Punj and Staelin 1983; Johnson and Russo 1984; Alba and Hutchinson 1987). Increasing experience has significant effects on consumer behavior (Larkin et al. 1980; Maheswaran and Sternthal 1990). These changes in behavior are important determinants of the success of different marketing activities. Three major effects of experience can be discerned.
One fundamental effect of experience is increased speed of usage. Virtually all learning processes result in increased speed of the performed task (Alba and Hutchinson 1987). For example, experienced users identify visual targets quicker than users with less experience with the task (Haider and Frensch 1999). Computer usage and search engine navigation have also been found to speed up with experience (cf., Moran 1980; Lazonder, Biemans, and Wopereis 2000). Repeat purchasing follows the same pattern, as repeat purchase decisions tend to be made more and more quickly over time (Hoyer 1984).
A second major effect of experience is related to information detection and discrimination. According to Alba and Hutchinson (1987), the ability to analyze information, isolating that which is most important and task-relevant, improves as experience increases. The expert restricts processing to relevant and important information. This is considered a key facet of expertise (Johnson and Russo 1983; Alba and Hutchinson 1987). The focus on relevant information also leads to less search (Punj and Staelin 1983).
A third effect of increasing experience is decreased cognitive effort. Simple repetition improves task performance by reducing the cognitive effort required to perform the task (Alba and Hutchinson 1987). People are lazy by nature and do not want to expend more effort than necessary (Wright 1975). Thus, as we become more experienced and familiar with a task we reduce the effort put into it, relying more on internal information and what we have already learned (Bettman and Park 1980). In fact, repetition of a task may even lead to performance that is automatic (Alba and Hutchinson 1987).
The three discussed effects seem to be common for most repetitive activities. They are especially prominent under conditions of time pressure and information overload, e.g., through the presence of distracting stimuli (Wright 1975; Hoyer 1984; Alba and Hutchinson 1987). These conditions are likely to be found on the Internet.
Time pressure is likely to be common when people use the Web. This may be due to the fact that the user is surfing the Web at work or in school (a very large amount of the reported usage, see for example Graphics, Visualization and Usability Reports, GVU 1998), where other activities or users compete for the time. Another reason may be the wish to keep costs down when surfing from the home or a cafe. Furthermore, if experience leads to increased speed of usage, this time pressure should be reinforcing to some extent, as later visits are made on less time. The number of distracting stimuli tends to be rather high on the Web, which is an information-intensive medium where many advertisers, links, pictures and more compete for attention (Dahlen 1997a; Khan and Locatis 1998).
As a result of the first effect, we expect experienced users to make shorter visits on the Web, as their performance speed increases. This may also be due to the fact that experienced Web users are better able to find what they are looking for and not engage in distracting activities. This leads to our first hypothesis:H1: Experienced users have shorter Web sessions than less experienced users.
The second and third effects of increasing experience should affect Internet usage in two ways. Firstly, experienced users should be less inclined to search around on the Web. They want to reduce cognitive effort and rely more on internal information. Thus, experienced users should rely more on an evoked set of Web sites they are already familiar with and visit fewer new Web sites. This leads to our second hypothesis:H2: Experienced users visit fewer new Web sites than less experienced users.
Secondly, Internet users should be more focused and discriminating. As they are better able to find relevant information and want to reduce their cognitive efforts, we expect Web usage to become more routine. Also, experienced users do not want to waste their time on irrelevant or unnecessary tasks. This should be reflected in the activities undertaken on the Net. This leads to our third hypothesis:H3: Experienced users use the Web more for practical and routine uses than do less experienced users.
Automaticity is a more extreme effect of experience than speed-up and reduced cognitive effort. Automaticity manifests itself in processes that can be performed with minimal effort and without conscious control (cf., Schneider and Schiffrin 1977; Alba and Hutchinson 1987). As the limit of advertising is not that of retention but that of attention (Krugman 1988), the impact of automaticity may be critical.
During automaticity, people learn what they attend to (Logan, Taylor, and Etherton 1996). Eye-movement tracking experiments show that with automaticity, task-redundant information is ignored at a perceptual rather than conceptual level of processing (Haider and Frensch 1999b). A major effect of automaticity is spatial indexing, resulting in a familiarity with potential target locations. This means that a person quickly learns to automatically identify the locations of potentially interesting information, thus enhancing visual search performance and attentional processing (Fournier 1994; Wright and Richard 1999). When locations are changed, performance is significantly worsened (Logan 1998). Locations of stimuli, in patterns, in relation to context etc., are more important during automatization than other factors such as element identities and color (Lassaline and Logan 1993; Chun and Jian 1998).
On the Web, consumers navigate using their visual attention. Most Web pages tend to have similar layouts, i.e., content in the middle of the screen and ads and links in the periphery of the screen. As Internet users become more experienced, they should automatically focus their attention on the content in the middle of the screen and ignore the peripheral stimuli.
According to Alba and Hutchinson (1987), the effects of automaticity will have an especially strong impact when the consumer is under time pressure or when the stimulus environment is complex. As we have previously concluded, both these conditions apply on the Web. Of particular importance is the fact that many Web sites are crowded with stimuli competing for attention. Thus, there is a strong likelihood that Web users will focus their attention through automaticity.
More specifically, we expect Web users to automatically screen out advertising (which mainly takes the form of banner ads, cf., IAB 1999) on the Web pages they visit, as they become more experienced. Thus, experienced users should have lower ad awareness than less experienced users. Ad awareness can be measured as both ad recall and ad recognition (Tellis 1998). This leads to our fourth hypothesis:H4: Experienced users have lower ad awareness (ad recall and ad recognition) than less experienced users.
The advertising context has been shown to affect consumer response to inserted ads, making this an important factor for advertisers to consider (cf., Lorch et al. 1994; Tavassoli, Shultz, and Fitzsimons 1995). Two user-related aspects of advertising context are involvement and pleasure (Soldow and Principe 1981; Golberg and Gorn 1987). Both of these could be expected to change with increasing user experience.
Related to increasing experience is the decrease in involvement. Inexperienced consumers engage in extensive problem solving (cf., Howard and Sheth 1969; Howard 1977; Bettman and Park 1980). With repetition and familiarity, task performance tends to become more routine and less involving (Howard and Sheth 1969; Howard 1977; Alba and Hutchinson 1987; Rossiter and Percy 1987, 1997). Consumers become less involved and try to minimize their efforts as they gain experience (Wright 1975; Bettman and Park 1980; Alba and Hutchinson 1987).
There is evidence that involvement with the advertising context has effects in several media. A number of studies show that involvement with a TV program affects advertising performance (Kennedy 1971; Soldow and Principe 1981; Pavelchak, Antil, and Munch 1988; Tavassoli, Shultz, and Fitzsimons 1995). Krugman (1983, 1986) finds that commercials placed in continuous involvement program formats have 2-3 times the ad recall rates of commercials placed in less involvement formats. Context effects are operating in the radio medium as well, with greater involvement programs yielding higher ad recall and brand communication effects (Norris and Coleman 1996). Similarly, increasing involvement (unless extreme) tends to benefit embedded advertising in sponsored events (Pham 1992). Studies on print media may be particularly interesting since the Web is similar to print media, where ads are embedded in the pages. This is especially true for traditional banner ad formats and Web sites (there are of course other multimedia formats of ads and Web sites, though they are less common). Buchholz and Smith (1991) argue that print media require the active participation of the audience since reading words is a relatively demanding cognitive task. Advertising in print media is therefore highly dependent on reader involvement. Studies indicate that advertising performance in terms of ad awareness and brand communication effects correlate positively with reader involvement, as the reader is required to actively read the ad and process it with some depth (Buchholz and Smith 1991; Greenwald and Leavitt 1984).
Another context factor that has been suggested to have an effect on advertising performance is pleasure. Pleasure may affect commercial performance positively and enhance brand attitude and ad recall (Golberg and Gorn 1987; Pavelchak, Antil, and Munch 1988). This is due to the fact that a positive affective state promotes learning by increasing consumer motivation (Pham 1992).
We expect Internet user involvement with the medium to decrease with experience. High involvement will prevail initially as the task of using the Internet is new and the novelty of the medium produces some fascination. With experience, the fascination wears off and usage becomes more routine (as stated in H3), leading to a low level of involvement with the medium. When it comes to advertising, the Web bears close resemblance to print media, where ads are embedded in the pages. Here, reader involvement seems to be crucial. Thus, we would expect a decrease in Web advertising performance as users become experienced. Furthermore, as usage of the Internet becomes more routine and practical, the amount of pleasure should be smaller. This also suggests that advertising performance should decrease.
Advertising performance on the Web is generally measured as banner ad clickthrough (cf., Briggs and Hollis 1997; Dahlen, Ekborn, and Morner 2000; Hoffman and Novak 2000). Increased brand attitude is a universal communication goal for all advertising (Rossiter, Percy, and Donovan 1991; Rossiter and Percy 1997) and thus another important basis for evaluating Web advertising performance (cf., Briggs and Hollis 1997; Dahlen, Ekborn, and Morner 2000). This leads us to the following hypothesis:
H5: Experienced users respond less to Web ads than do less experienced users. More specifically, experienced users a) click less on banner ads and b) exhibit a smaller change in brand attitude in response to banner ad impressions.
Data were collected in three different studies of Swedish Internet users. Sweden is known for its extremely high Internet penetration, the highest in the world. In 1996 it was over 20% and in 2002 it is close to 70% of the population, defined as those who use the Internet frequently (NUA 2002). The demographics have always been rather evenly distributed compared to other countries. The gender ratio was 68% male/32% female in 1997 and 53% male/47% female in 2001 (Jupiter MMXI 2002). About 58% use the Internet daily; this figure has been constant between 1997 and 2001 (Dahlen 1997b; Jupiter MMXI 2002). Multiple usage locations (home, work, school) have always been common in Sweden (Dahlen 1997b).
The usage areas in Sweden are similar to those in the United States. Entertainment and information search are the main areas. The top ten most visited Web sites in November 2001 included three American sites and seven domestic sites, consisting mainly of portal sites and magazine sites (Jupiter MMXI 2002). With this information about Swedish Internet users in mind, we move on to the three studies.
The aim of this study was to test hypotheses H1-H3. In order to do this, the behaviors of Internet users with varying levels of experience had to be compared. This was done in a survey of Swedish Internet users in 1997.
A random sample of 1100 e-mail addresses to Internet subscribers was drawn from the customer database of Sweden’s second largest Internet service provider Telia. The subscribers were both private users and company users. They received an e-mail invitation to participate in the study and a link to the questionnaire site. Four hundred thirteen respondents completed the questionnaire, yielding a response rate of 38%.
For measurement of Internet experience, the respondents were asked how long they had used the Internet. This corresponds with the conceptualization of Internet experience in Ward and Lee (2000) and Novak, Hoffman, and Yung (2000). The question was measured on an open-ended ratio scale, where respondents typed in any number of months. Similarly, the length of Internet sessions was measured with an open-ended question: "how long is a typical Internet session for you?", where respondents typed in any number of hours and minutes. The proportion of new Web sites visited was measured with the question "approximately, how big a share of your Web site visits are visits to new, previously unvisited Web sites?". The answer was given in percentages on a ratio scale. The form of visit was measured with two questions: "How often do you engage in activities with no specific purpose or activities you had not planned beforehand?" and "How often do you engage in focused, practical activities decided beforehand?". The answers were given on a five-point scale (1=never, 5=every visit).
The aim of this study was to test hypothesis H4 (along with other advertising effects). In order to do this, we needed to compare users with varying levels of Internet experience and their reactions to Web ads. This was done in a study where Internet users were unobtrusively observed, using cookie files, as they were exposed to banner ads and later surveyed. Visitors to three high traffic (top 50), Swedish magazine Web sites (expressen.se, varbostad.se, and ostgotacorrespondenten.se) were intercepted with a pop-up dialog box inviting them to participate in a study on marketing. Those who agreed were given cookie files that registered the banner ads they were later exposed to. An invitation to participate in a survey was sent out via e-mail two to five days later. In this survey, ad awareness was measured and matched with each respondent’s behavior from the cookie file. The response rate in the first recruitment step was 23%. In the second step, 78.6% completed the questionnaire. One thousand two hundred and four complete responses were collected. The study was conducted in 1999.
Internet experience was measured the same way as in study 1. Ad awareness was measured as brand-prompted ad recall ("in the last few days, have you seen a Web ad for brand x?") and as ad recognition ("do you recognize this ad?"), on a dichotomous yes/no scale. These measures were based on the recommendation by Tellis (1998).
The aim of this study was to test hypothesis H5 (along with other advertising effects). In order to do this, we needed to compare users with varying levels of Internet experience and their reactions to Web ads. This was done in a study similar to study 2, where Internet users were unobtrusively observed using cookie files, as they were exposed to banner ads, and later surveyed. Clickthrough was also automatically reported. This time, visitors to Sweden’s most visited web site, Passagen.se (portal site), were intercepted. The response rate in the first recruitment step was 29%. In the second step, 75% completed the questionnaire. A total of Fourteen thousand six hundred responses were collected. The study was conducted in 2000.
Internet experience was measured as length of usage on a five-point ordinal scale (see tables II and III in the results section), taken from Ward and Lee (2000) and Novak, Hoffman, and Yung (2000). Brand attitude was measured with a seven-point semantic differential scale (1=brand X is the best in the product category, 7=brand X is the worst in the product category). For each product, the brand name and the product category were substituted in the statement. This measure was based on the recommendations by Rossiter and Percy (1987, 1997) and corresponds closely with other measures of brand attitude (Dahlen, Ekborn, and Morner 2000). A single item measure was used as there is a great risk of irritation and mindless response behavior (Drolet and Morrison 2001) on the Internet. Furthermore, the extra items in the measure have been shown to have marginal information value (Drolet and Morrison 2001).
Demographics and purpose of the Web site visits were controlled for in the studies. They had no effects on the results.
In study 1, Internet users were divided into quartiles, based on their experience. Differences were rather small – the least experienced quartile consisted of people with five months or less experience, whereas the most experienced quartile consisted of people with ten months or more experience. This is not so surprising, as the Internet was new to most users at the time and overall experience was thus comparably low. Therefore, comparisons were only made between these two even-sized extreme quartiles. The reader should bear in mind that the differences in experience are small and compare with the experience groups in the two following studies, where the time-span is greater. The results are depicted in Table I.
Table I. Hypotheses H1-H3. Internet experience and user behavior
In order to test the hypothesis that Web sessions become shorter with experience, mean comparisons were made between high experience users and low experience users. Low experience users were found to spend more than twice the amount of time on average (mean value = 59 minutes) during a typical Web session than did high experience users (mean value = 26 minutes). Hypothesis H1 is thus supported: experienced users have shorter Web sessions than less experienced users.
Mean comparisons were also made between the two user groups with respect to the proportion of new Web sites they visit. Once again, low experience users produced a mean value (45.3%) double the mean for high experience users (23.3%), indicating that the former are more frequent visitors to new Web sites. H2 is thus supported: experienced users visit fewer new Web sites than less experienced users.
In order to test the hypothesis that experienced users are more practical and routinized in their Web usage, cross-tabulations were conducted for "no purpose or plan" visits and "practical and planned" visits, respectively. Among high experience users "no purpose or plan" visits were clearly under-represented (3.8% every visit), whereas "practical and planned" visits were significantly over-represented (67.1% every visit). The opposite patterns were found for low experience users. Hypothesis H3 is thus supported: experienced users use the Web more for practical and routine uses than do less experienced users.
The data in study 2 on Internet experience was divided into five groups in order to match the scale in study 3. Cross-tabulations were conducted to compare ad recall and ad recognition between the user groups. The results are shown in Table II.
Table II. Hypothesis H4. Internet experience and ad recall and ad recognition
As can be seen, both types of ad awareness follow a clear pattern. The least experienced users display the highest levels of ad recall (28.7%) and ad recognition (39.1%). The levels drop through the more experienced user groups, and the lowest levels are found in the most experienced user group (15.7% and 23.1%). H4 is thus supported: experienced users have lower ad awareness than less experienced users.
In order to test hypothesis H5, clickthrough rates and brand attitude changes from mere banner ad impressions were compared between the user groups. The results are depicted in Table III.
Table III. Hypothesis H5. Internet experience and brand attitude change
A cross-tabulation of user experience and clickthrough rate shows that there is a marked difference between the least experienced users and the other groups in the propensity to click on banners. The least experienced users are four times more likely to click on banner ads (2.3%) than the other users (0.3-0.6%).
To test the effects of banner ad impressions (without clickthrough), brand attitude was compared between users exposed to certain banner ads and users who were not exposed to the banner ads, using t-tests. As can be seen in table III, there is a significant difference in brand attitude between exposed and non-exposed consumers in the least experienced user group. This indicates that banner ad impressions have a positive effect on brand attitude for these users. The comparisons between exposed and non-exposed consumers in the other, more experienced, user groups produced no significant results. With respect to brand attitude, more experienced users do not seem to be affected by banner ad impressions.
Hypothesis H5 is thus supported: experienced users respond less to Web ads than do less experienced users. More specifically, experienced users a) click less on banner ads and b) exhibit a smaller change in brand attitude in response to banner ad impressions.
The results in this article indicate that Internet experience is an important factor for marketers to consider. Three different data sets, spanning over a period of four years, all provide evidence on the effects of increasing user experience. This suggests that the investigated effects are related to the process of learning that all Internet users go through. Internet experience is thus an enduring phenomenon, which deserves increasing attention in the future.
As experienced Internet users change their behavior and make shorter, more practical and routine visits to a decreasing number of familiar Web sites, they become harder to attract on the Net. This should be a bad thing for marketers, as the space for marketing decreases. However, in one respect, the effects may be positive. Shorter, routine visits to mostly familiar sites could have one speculate that, contrary to popular belief, Internet users should become more loyal with experience. If a Web site manages to get into the Internet user’s evoked set, it may take a very favorable position and become more immune to competition as the user gains experience. E-loyalty could thus be the most important marketing weapon in the future (cf., Reichheld and Schefter 2000).
Considering this, Web marketing should be differentiated with respect to user experience. One aspect of this is, of course, that more focus should be put on marketing towards new users. Firstly, this presents the potential advantage of gaining loyal consumers. Secondly, as far as "traditional" Web marketing in the form of banner ads and the like goes, new users are the only target group where marketing seems to have a major effect. Marketing to more experienced users in this traditional way may be a waste.
A second aspect of this is that new Web marketing formats are needed to influence experienced users. One way is to challenge the automaticity of the users and try to gain attention through new page layouts and challenging (or integrated) ad placements. Another way is to offset the low user involvement by making the Web visit more compelling. Activities should be designed to engage users more actively in the advertising context.
In conclusion, the view of Internet users both among practitioners and researchers needs to be altered. Instead of focusing only on how the medium develops, we need to learn how the users develop. Internet users cannot be seen as static in their behavior. Rather, we must learn how usage changes and be able to predict how users will respond to marketing as time progresses. This is important as more and more users become experienced.
Turning to more general theoretical issues, the present studies have shown that experience is an important factor, not only in product search and usage and performance of specific tasks, or reactions to advertisements, but also in general media usage and response.
There are important limitations with the methodology in this article that need to be considered. Firstly, the frequency measures in study 1 are based on self-reports. This is always a potential source of error (for a review, see Schwartz 1999). The absolute values should therefore be viewed with caution. However, the focus of analysis is relative values and comparisons, in which errors should cancel each other out. It should be noted that all the measures in the first study are subject to qualitative meanings that are hard to discern and the results should be viewed in light of this.
The second limitation is the conceptualization of Internet experience as length of usage. This corresponds well with previous work by Ward and Lee (2000) and Novak, Hoffman, and Yung (2000). However, there are other ways to measure experience, such as frequency of usage, which was used by Bruner and Kumar (2000) in their study. The frequency measure could possibly yield different results. Furthermore, the relationship between the two different measures deserves more attention.
The article treats user involvement as a given variable, which decreases with experience and thus has important effects. However, being treated as a given, user involvement is never actually measured. The article builds on three different studies, spanning four years. While this could be viewed as a strength, it can also be considered a weakness. As the Internet is such a new medium, many changes have occurred in the meantime. The influence of these changes is unknown.
Finally, we should note that only Swedish users have been studied. Therefore, it is important to consider the ability to generalize the results. As the studies build on general theories of human behavior, it is our firm belief that they hold true for all countries. However, as Sweden is a very Internet-savvy country, it is possible that the effects appear quicker and are of greater magnitude than would be found in countries with less Internet usage.
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Micael Dahlen is an assistant professor at the Center for Consumer Marketing, Stockholm School of Economics, Sweden. His research area is advertising, in which new media has received special interest. He has published articles in journals such as the Journal of Advertising Research, International Journal of Advertising and Journal of Marketing Communications.
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