Affect As Information: the Role of Affect in Consumer Online Behaviors

Lan Xia, University of Illinois at Champaign-Urbana
ABSTRACT - As E-commerce grows, obtaining further understanding of consumer online behaviors becomes an urgent issue facing both researchers and marketers. This paper focuses on consumer affect as an important construct for understanding their online behavior. Applying the Affect-as-Information model, this paper proposes that consumer affect interacts with their online purposes to influence consumer online browsing behaviors. Specifically, it is proposed that positive affect will lead to shorter browsing time and less detailed information processing when the online purpose is task oriented; and longer browsing time and more detailed information processing when the online purpose is pure entertainment. Negative affect works the opposite way. Two studies were conducted and the results supported the hypotheses. Implications of the model were discussed.
[ to cite ]:
Lan Xia (2002) ,"Affect As Information: the Role of Affect in Consumer Online Behaviors", in NA - Advances in Consumer Research Volume 29, eds. Susan M. Broniarczyk and Kent Nakamoto, Valdosta, GA : Association for Consumer Research, Pages: 93-99.

Advances in Consumer Research Volume 29, 2002     Pages 93-99

AFFECT AS INFORMATION: THE ROLE OF AFFECT IN CONSUMER ONLINE BEHAVIORS

Lan Xia, University of Illinois at Champaign-Urbana

ABSTRACT -

As E-commerce grows, obtaining further understanding of consumer online behaviors becomes an urgent issue facing both researchers and marketers. This paper focuses on consumer affect as an important construct for understanding their online behavior. Applying the Affect-as-Information model, this paper proposes that consumer affect interacts with their online purposes to influence consumer online browsing behaviors. Specifically, it is proposed that positive affect will lead to shorter browsing time and less detailed information processing when the online purpose is task oriented; and longer browsing time and more detailed information processing when the online purpose is pure entertainment. Negative affect works the opposite way. Two studies were conducted and the results supported the hypotheses. Implications of the model were discussed.

INTRODUCTION

As more businesses and consumers adopt the Internet, it is becoming a part of people’s everyday life. Available research on consumer online behaviors has mainly focused on the economic aspects of online businesses such as how it reduces transaction cost or consumer information search cost (Bakos 1997). However, one important aspect that has not been examined in much detail is consumers’ subjective experiences. The role of emotion/affect [Emotion and mood distinguished based on whether there is a salient object of the feeling. Affect is a broader category of things with positive or negative values which include emotion and mood (Clore et al. in press). The three concepts are not distinguished in this paper.] has been recognized as an important construct in understanding (traditional) consumer behaviors. Therefore, it should not be left out of the study of consumer online behavior.

Empirical research showed that people may enjy surfing the Web so much to even have time pass by unnoticed (Novak, Hoffman and Yung 2000), or experience the frustration of not being able to find what they look for (GVU Internet survey [http://www.cc.gatech.edu/gvu/user_surveys]). People experiencing both positive and negative feelings while browsing online, raises an important research question: how these feelings influence consumer browsing behaviors on the Internet.

Assuming that people may bring different "feelings" into online activities and/or that surfing on the web itself may induce such "feelings", the purpose of this paper is to use the Affect-as-Information model to investigate the influence of affect and its interaction with consumer online purposes (information processing goals) on consumer online behaviors such as viewing time. The proposed model is expected to enrich the theoretical basis of studying E-commerce, as well as provide managerial implications of how to use the Internet as a marketing tool more effectively.

LITERATURE REVIEW

Effort has been exerted to understand consumer online experiences. Hoffman and Novak (1996) used the concept of flow to model consumer online behaviors. The flow state has desirable outcomes in consumer online behaviors (Hoffman and Novak 1996). It enhances consumers’ experience and facilitates learning. Although flow is not exactly an emotion, it definitely is a pleasant feeling (Wyer and Srull 1989). Flow represents a desirable and pleasant feeling online. But it is not the only feeling that consumers experience. Consumers get frustrated when they are not able to find things they want and annoyed by misleading information as much as they are excited by the new media. Since consumers have these complex and versatile experiences, a broader view of consumer affect online is needed.

Consumer Affect on the Internet

Affect could be endogenous or exogenous. Consumers may bring affect, induced by activities or incidents outside of online environment, into online shopping/browsing. They can unconsciously misattribute the affect to it, thus, influencing online shopping behaviors. On the other hand, affect can be induced by the online shopping environment itself. The former (exogenous) source of affect has been used widely in the study of affect (Clore, Schwarz and Conway 1994), and the later (endogenous) source has been used to study consumer shopping behaviors in the physical environment (Donovan and Rossiter 1982). Empirical tests showed that emotions induced by the environment mediate consumer behaviors. Consumers spent more time, purchased more items, and enjoyed it more when they perceived the environment as pleasant and experienced a moderate level of arousal.

The online environment has its "atmospheric aspect" as well. Eroglu, Machleit, and Davis (2000) applied the Stimulus-Organism-Response framework to online shopping environment and proposed that online retailers, similar as their "brick-and-mortar" counterparts, can provide a "store" atmospher that influences shopper’s experiences. Their empirical tests showed that appropriate "store" atmosphere increased shoppers’ positive feelings. Empirical findings in media research supported the idea that the virtual environment can elicit emotional states that are fundamentally the same as the natural physical environment do (Reeves and Nass 1996). Reeves and Nass argued that people treat computers, television and other media like real people and places. Their research showed that equating mediated and real life is neither rare nor unreasonable. They found that people quite often unconsciously and automatically reacted to the mediated environment as they did in the real environment although, on retrospect, they themselves may have thought it was surprising.

Therefore, if online environment is powerful enough to elicit affective feelings and if these feelings may have substantial influence on information processing and product evaluation, it is important to further investigate how these feelings influence consumers’ online behaviors.

Effects of Affect on Information Processing

As Bagozzi (1997) pointed out, emotion/affect operates at various stages of consumer decision-making and it serves to motivate actions, guide information processing and regulate the pursuit of goals. Research has established that affect influences people’s information processing styles, product evaluation and judgment. Positive affect is associated with global, heuristic processing while negative affect is associated with local, diagnostic processing (Bless et al. 1990). Bless et al. showed that people in a positive mood tended to be persuaded by both strong and weak arguments while those in a negative mood were only persuaded by the strong arguments. In addition, positive affect diffuses attention (Mackie and Worth 1989), causes broader categorization (Sinclair and Mark 1992), and leads to more creativity (Murray et al. 1990) than negative affect. Finally, positive affect may lead to favorable product evaluation and judgment when the real source of affect is not salient (Clore et al. in press).

However, negative affect is not always dysfunctional in consumer decision-making. For example, Luce, Bettman and Payne (1997) showed that a moderate level of negative emotion during the process of decision making may signal the importance of the task and encourage more detailed information processing. Further, such negative affect may mobilize the allocation of resources and lead to even higher performance in decision-making (Pieters and Raaij 1988).

Affect as Information

The Affect-as-Information hypothesis models the interface between emotion and cognition. The model suggests that affect/mood operates as a source of information (Clore 1992; Schwarz and Clore 1988). People draw information from their feelings in much the same way as they draw information from their behaviors. Further, people use their feelings to interpret their situations in the environment. People in a positive mood will interpret the environment as benign. Hence, they process information globally and heuristically. In contrast, those in a negative mood will iterpret the environment as problematic and they will process information locally and diagnostically. People tend to use Affect-as-Information heuristic when the evaluation objective is affective in nature, when information is too complex, or when there are time constraints (Clore et al. 1994). Recent studies conducted by Pham, Cohen, and Pracejus (2000) showed that affect provided judgmental responses that are faster and more predictive of people’s thoughts compared to careful evaluation and integration of information.

Martin et al. (1993) further suggested that people not only drew information from their feelings, but that the interpretation of their feelings also had a motivational effect. They argued that it was not people’s feelings per se that caused them to engage in different types of processing. Rather, it was people’s interpretations of their feelings. Therefore, with different interpretations, the same affect could have different motivation effects. They hypothesized that if affect was interpreted as the attainment of a goal or approaching a goal, people in positive mood may interpret their positive feelings as a sign that they had attained or are close to attainment of the goal. Meanwhile, people in negative mood may interpret their feelings as a sign of being still far from attaining the goal. As a result, people in positive mood would be more likely to stop their current goal-directed activity than those in negative mood (Carver and Scheier 1990). On the other hand, if positive affect were interpreted as evidence of their enjoying a task and negative affect as evidence as their not enjoying it, then people in positive affect would persist in the activity longer than those in negative affect, assuming they will continue doing what they intrinsically enjoy (Murray et al. 1990). Experiments by Martin et al. (1993) supported their hypotheses. When an enjoyment criterion was used to evaluate the task, happy subjects persevered on the task longer than sad subjects. The reversed effects were observed when a performance criterion was used.

People get on to the Internet for various reasons. They may have a product in mind to purchase, or look for product information, or just browse and enjoy themselves. Therefore, the influence of affect on their behaviors may well depend on their purposes online.

CONSUMER AFFECT AND ONLINE BEHAVIOR

The Internet expands consumers’ shopping horizon. It provides consumers with utilitarian values as well as entertainment values. For example, visiting P&G’s Tide website (www.tide.com), consumers not only obtain information about Tide but also find information on how to clean different types of stains, read news about Tide racing team, and enter sweepstakes. Hence, purchasing is not the sole purpose of shopping on-line. People use Internet for different purposes. According to GVU’s most recent survey of Internet users [http://www.cc.gatech.edu/gvu/user_surveys, October, 1998], the primary use of the Web includes personal information (73.6%), work (65.9%), education (61.4%), entertainment (60.1%), shopping (52.4%), communication (35.4%), and even time wasting (37%).

Consumers may exhibit various types of behaviors depending on their online purposes. Much consumer behaviors are goal-directed (Bagozzi 1997). Consumers may shop online with different level of goals: specific goals (e.g. purchase a product, find product information), abstract goals (e.g. find something that they might want to buy), or no goals atall (e.g. browsing for recreation). All these activities are important for marketers because on one hand, they want to achieve sales on the Internet, and on the other hand, they are also competing for consumers’ visits and viewing time in order to gain precious online advertising "clicks" and "impressions".

The importance of differentiating consumers’ purposes online lies in the different implicit criterions that they may use for evaluation of their behaviors. On the Internet, there is so much information and so many product options available that it is unlikely for consumers to consider all alternatives. Therefore, as consumers "navigate" through the Internet, they need some feedback, criterions, or "stopping rules" to guide their navigation. Specific-goal directed behaviors are more likely to be evaluated by some performance criterion such as "Have I searched enough information", or "Have I looked at enough options". On the other hand, when people are online just for general purpose browsing, their purposes are mainly enjoying themselves. They may implicitly think, "Is this fun", "Do I enjoy this". Therefore, an enjoyment criterion is more likely to be used. In situations where explicit feedback is lacking, "how do I feel" could be an important source that consumers draw information from to answer these questions and guide their behaviors.

Both positive and negative affect could be induced in the Internet environment or brought into online activities. Based on the Affect-as-Information hypothesis and mood management model, the same affect will have differential impacts on consumer online behaviors, such as viewing time and information searching and processing when their purposes of using the Web are different. It is proposed that consumer online affect influences their behaviors and the influence is moderated by their online purposes (goals).

Online purposes can be categorized as 1) task-related and 2) pure browsing without a specific goal. It is assumed that when the purpose is task-related, some performance criterion will be used as evaluation and when the purpose is browsing only, enjoyment criterion will be used to guide behaviors. The implications of affective feelings in the above two categories are different. When performance criterion is activated, positive affect is more likely to be interpreted as attaining or making progress toward the goal. As a result, people will stop the activity sooner. In contrast, negative affect is more likely to be interpreted as being insufficient to attain the goal and/or still far from attaining the goal. Therefore, people will persist in the activity for a longer period of time.

When browsing and having fun is the major purpose of being online, some enjoyment criterion is likely to be applied. In this situation, positive affect is more likely to be interpreted as a sign of enjoyment and motivate people to continue engaging in the activity (Martin et al. 1993). Therefore, they will stay longer in the activity and visit more pages. They infer that they are hedonically rewarded by the current activity. To maintain their positive mood, they will continue engaging in the activity (Wegener, Petty and Smith 1995) and process information in more details. In contrast, negative affect is more likely to be interpreted as not enjoying the activity and motivate people to stop earlier. As a result, they will spend less time on the activity and visit fewer pages.

A pretest and two studies were conducted to test the hypothesized effects.

FIGURE 1

TIME SPENT ON TASK - STUDY 1

METHODS AND RESULTS

Pretest

To test the hypotheses, a pretest was conducted first to verify the assumptions that different implicit criteria are used under different affect conditions and online purposes. The pretest also served to provide information for choosing the appropriate stimuli for the studies.

A questionnaire was constructed. Ninety-nine students answered the questionnaire. Each subject was asked to imagine a hypothetical situation that involved a specific affect condition (happy or sad) and a purpose online (purchasing or browsing). They were asked questions about their feelings in that particular situation and their thoughts in terms of how to decide whether to continue or stop. In addition, a list of six types of websites was provided and subjects were asked to rate each website's utilitarian and recreational value. The purpose was to identify appropriate websites that attract both users for recreational purposes and utilitarian purposes and to be used in the following studies.

A series of ANOVAs were run on the 'stopping rule' measurement. Results supported the assumption that when purchase was the purpose of being online, degree of information search was a major criterion that people would use to decide whether they will keep searching (F(l, 98) = 8.68; p=0.004). When the purpose of being online was just browsing, enjoyment was a major criterion that they use to decide if they should stop (F(1,98)= 14.68; p < 0.000). Results showed that people do use different criteria (i.e. 'stopping rules') in guiding their online behaviors when in different mood and with different purposes.

Experiments using online environment as stimuli were then conducted to directly test the hypotheses. Based on the pretest, online auction store was selected as the type of stimuli because it provided both utilitarian and recreational function and there was no significant gender difference in perceptions [On1ine auction stores, travel/trip planning sites, and bookstores were perceived as providing both utilitarian and entertainment values. But there was significant gender difference. Females perceived apparel stores, bookstores and travel/trip planning sites as provide more entertainment value than utilitarian values, compared to males.].

Study 1

Experiment Design and Stimuli

Study 1 was a replication of Martin et al. (1993) study using online environment as the context. The hypotheses were tested using an affect (happy vs. sad) x evaluation criteria (performance vs. enjoyment) between subjects factorial design. Mood was induced by asking subjects to write an event that happened to them and made them happy (or sad). This mood induction technique has been used widely and proved to be effective (Schwarz 1983). The online task was specified as browsing some websites and forming an impression of it. In the enjoyment condition, subjects were instructed to continue as long as they enjoyed it and stop when they no longer enjoyed it. In the performance condition, subjects were instructed to stop when they felt they had enough information to form an impression and continue as long as they felt not having enough information. This instruction was adapted from Martin et al. (1993).

Major online auction stores were identified using a search engine (www.yahoo.com). Six stores were randomly selected and listed in an HTML page with each store shown as a link. A computer program was used to capture the time subjects spent and pages they visited during the task.

Subjects and Procedure

Thirty-five undergraduate students participated in the experiment. The experiment was conducted in a big computer lab with IBM PCs. Subjects participated in groups of 2 to 5. They were seated separately to prevent from influencing each other and performed the task individually.

Subjects were told that the experiment session included two separate tasks. After the mood induction task, subjects were instructed to start the online task, where they were provided with either a performance criterion or an enjoyment criterion. Immediately after they finished, they were given a questionnaire where their mood state was measured and some demographic information was collected. The experiment resulted in 31 usable data points. Cell size ranged from 6 to 9.

Analysis and Results

To check mood manipulation, subjects were asked, "how did you feel when you wrote the story", and "how do you feel now". The six- item pleasant/unpleasant measurement was adapted from Larsen and Diener (1993) [The items are originally from Russell (1980).]. Mood manipulation was successful. However, there was a significant mood change after the task, with happy subjects became slightly unhappier and sad subjects became significantly happier. After screening out subjects whose mood changed significantly, 26 subjects were left for analysis.

FIGURE 2

TIME SPENT IN EACH STORE - STUDY 2

Subjects spent an average of 10.34 minutes on the task, visited 3.7 stores and 32.8 webpages in total. A two-way ANOVA showed a significant interaction between mood and evaluation criteria (F(1,25)=5.78; P=0.025). Happy subjects spent more time than sad subjects when "enjoyment" was the criterion used for processing (mean=725.75 and 578.89 seconds respectively). However, they spent less time than sad subjects when "performance" was the criterion used for processing (mean =520.2 and 637.5 seconds respectively (See Figure 1). Number of pages visited showed the same pattern (F(1,25)=4.99); P=0.036). The results supported the hypotheses that the time spent browsing on the Internet depend on both subjects' affect and the criteria used to evaluate their performances.

Further examination of time spent per store showed some clue of different information processing styles of happy/sad subjects under different processing criteria, although the statistics are not significant (F(1,25)= 1.65; P=0.21). Happy subjects spent more time in a store than sad subjects under enjoyment criterion. This may suggest that they processed information in a more detailed way while the sad subjects did that in a more holistic way or at a surface level. Under the performance criterion, the effect is reversed. Happy subjects spent less time per store than sad subjects. It may signal that they processed information in a more holistic way while the sad subjects did in a more detailed way.

Discussion

Study 1 showed that subjects' mood did influence their online browsing behaviors. Further, the influence of mood is moderated by the criteria that they used to evaluate their performance. Results of study 1 were consistent with previous findings that the influence of affect on behaviors interacted with different evaluation criteria, which could be derived from different purposes of the same activity. The nonsignificant result of amount of information processing per store could be due to the small sample size and/or the small number of stores provided (due to ceiling effects).

In study 1, instructions were adapted from Martin et al. and the criteria subjects used for monitoring their behaviors were given, instead of having them self-inferred from purposes online. The Internet is becoming a market place where consumers can make purchase and/or have fun. The pretest showed that consumers may use different criteria to evaluate their activities when they have different purposes/goals in mind. Therefore, it is important to distinguish Internet users with different purposes (goals) because of the different criteria they may use to evaluate performance. Study 2 was then conducted to test the hypotheses when only online purposes were instructed and evaluation criteria were kept implicit. Also, a larger sample size was used to increase power.

Study 2

Overview

Study 2 was the same two by two design as study 1. Another six online auction stores were added to expand the store list and prevent the ceiling effect. Instructions were changed to fit a more general 'online shopping' context. Subjects were instructed either to just browse the websites, or look for an electronic product to purchase. They were told to continue or stop on their own accord. No specific stopping criteria were given. Sixty-seven undergraduate students participated in the study. Cell size ranged from 14 to 18. The same mood induction method as in study 1 was used. In addition to demographic information, subjects' general attitude toward online shopping, usage of the Internet, and interests in the products shown in the websites visited were also measured.

Analysis and Results

The mood manipulation was successful. Similar to study 1, mood, especially the negative mood, was diluted slightly during the task. However, the overall mood change during the task was not significant (t (65)=0.452; p=0.653).

The mean time spent on the task was 14.96 minutes. Subjects visited an average of 7.3 stores and 48.4 pages. Similar to study 1, a two-way ANOVA showed a significant interaction between mood and online purposes (F(1,66)=7.995; p=0.006). Subjects in the positive mood spent significantly more time on the task than those in the negative mood when asked to j ust browse (mean= 1183.93 and 830.28 seconds respectively), but spent less time (not significant) than those in the negative mood when asked to purchase (mean=770.47 and 830.56 seconds respectively). A simple main effect was identified for subjects in positive mood. They spent more time when asked to browse than when asked to purchase (F(1,66)=5.79; p=0.019), while those in negative mood were not influenced by task purpose instructions.

Time spent per store also showed the same pattern as in study 1 and the interaction was significant (F(1,66)=5.22; p=0.026; see Figure 2). Subjects in positive mood spent more time in each store than those in negative mood while browsing, but spent less time per store than those in negative mood while considering purchasing. No significant results were found for the number of pages as in study 1. This could be due to the technical problem when the data did not capture subjects' browsing activities when a website had the function of opening another window while surfing.

Using subjects' interest in the products shown in these websites and their general usage of the Internet as covariates showed that those who were interested in the products (F(1,63)=8.498; p=0.005) and who used the Internet more frequently for various purposes (F(l, 63)=8.367; p=0.005) tended to spend more time on the task. Subjects' attitude toward online shopping and length of using the Internet had no effect.

Discussion

Study 2 extended study I by using online purposes as instructions while leaving the evaluation criteria that subjects used implicit. Similar results were obtained compared with study 1. Subjects who were in a positive mood spent more time on the task when instructed to just browse and less time on the task when instructed to think of purchase. On the other hand, subjects who were in a negative mood showed the opposite trend. Study 2 further substantiated the results of study 1. In addition, the nonsignificant effect on time spent per store was improved in study 2. The interaction effects on time spent per store suggested that consumers' online information processing styles may also be influenced by combined effects of online purposes and consumers' subj ective feelings.

However, the changes of instruction on subjects who were in a negative mood was not significant although the trend was as predicted. This could be due to the changes of mood during the task. Since mood was exogenously induced, subjects may have used the task as a mood management tool to improve their mood. In addition, the nonsignificant effect may have also been due to the weak manipulation of the purchase condition. Subjects were asked to think of purchasing something, but they were not actually making a purchase decision (i.e. buy a specific product).

IMPLICATIONS

The two studies conducted suggest that consumers' mood does influence their online behaviors. The influences of mood also depend on consumers' interpretation of their feelings given different online purposes. The results provide useful managerial implications as to the importance of providing an appropriate online atmosphere to consumers based on their online purposes in order to facilitate their navigation and information processing.

The proposed model suggests that the influence of affect on online behaviors is moderated by consumers' purposes of engaging in the activity. They are more likely to spend more time when they are in a positive mood and enjoy themselves. However, if they are very task-oriented, positive mood will actually have the opposite effect. Adar and Humberman (1999) suggested that there were different surfing styles. Their analysis of Web statistics showed that people's surfing patterns were significantly different across types of websites. They did not specify what were the three categories of websites they studied, but presumably different surfing patterns may be related to consumers' purposes of surfing and/or characteristics of the websites (utility oriented vs. hedonic oriented). Therefore, marketers can use the characteristics of their site and/or visitors' searching patterns (e.g. searching for specific information or randomly searching) to sense their goals. Based on consumer goals, marketers can anticipate their surfing behaviors and apply strategies to influence behaviors through consumers' affective feelings.

The model shows that a desired state is when people are in a positive mood and enjoy surfing on the Web because they are more likely to continue the activity and seek more information. Marketers can guide visitors when they have no specific purposes and convert browsers to buyers. According to the GVU survey, 71 % of Internet visitors browse without an intention to buy, but at the same time, 65% of them are saying that 25-50% of their purchases begins without any intentions. Technologies such as collaborative filtering have proved to be effective tools (Lach 1998).

When people are in a positive mood but perceive their activity as specific-task related, they will stop sooner if they feel that they have had enough information. In this situation, marketers may focus on strategies that stretch their viewing time. Adar and Humberman (1999) suggested that links of the website can be strategically structured to prevent direct achievement of consumer goals, but within the number of pages that consumers were willing to visit (not to frustrate them), may help marketers to obtain more viewing time and page visits. Further, trying to induce some enjoyment criterion may shift their focus and encourage them to continue the activity. In addition, providing incentives such as coupons when visitors finish their tasks may attract their attentions and extend their visits to an enjoyment activity (Adar and Huberman 1999).

Consumer negative affect may not be a good situation for marketers, especially when the major purpose for consumers is to enjoy themselves. In this situation, efforts could be devoted to change their mood state. One way is to frame the messages in an uplifting tone (Wegener et al. 1995). This could be effective when consumers do not attribute the negative affect to the current browsing episode and anticipate mood improvement. According to the mood management view, people in the negative mood are likely to process information, which may change their mood state (Wegener et al. 1995). The current research showed that most people perceive online activities as fun, and therefore, are very likely to employ mood management techniques when the negative mood is induced by activities/incidents other than online shopping/browsing. This could further be incorporated with inducing some task/performance criterion so as to motivate them to continue.

When people in a negative mood are carrying some specific tasks, the model suggests that they are likely to spend more time processing information in order to attain the goal. As a result, they could make a good decision (e.g. purchase) but have an unpleasant experience of the decision process. In this situation, marketers can provide them with feedback to encourage the activity and induce positive affect, especially at the end of the task. This will prevent the negative affect/experience being encoded in their memory and influence future evaluations of the product/service and the provider.

Consumers may have multiple purposes for being online. Therefore, multiple criteria are available for evaluating their online behaviors. In such situations, marketers can influence the criterion they used by shifting their focus to the enjoyment of the activity and induce positive affect, thus, extending their time spent and the amount of information processed. For example, they could use available technology to interact with visitors by providing feedback or instructions in order to enhance their intrinsic interests (Murray et al. 1990; Sansone, Sachan and Weir 1987). This would mainly apply to adding the enjoyment element to task related purposes and lead consumers to realize that the activity is more enjoyable than they thought.

To summarize, the Internet makes information more available and accessible. Therefore, it dramatically reduces consumer information searching cost. However, consumers still need time and (cognitive) effort in order to process information. When the amount of information greatly exceeds consumers' processing capacity, and they do not have the time to exhaust their search, one available heuristic to follow could be their feelings. Consumers pay marketers to access information on the Internet through their time and attention instead of money. To be successful online, marketers have to compete for consumers' time and attention. The proposed model suggests that one way to attract consumers' time and attention is by monitoring their affective feelings and processing goals.

The results of the studies provided useful managerial implications. However, there are also some limitations that need to be overcome in future research. First of all, in both studies, the source of mood state was exiguousness. Mood (i.e. happiness or sadness) was induced in the laboratory setting by a technique that is not related to online activities. In practice, marketers have no control over such mood states. Therefore, endogenous affects such as excitement, frustration and irritation that are induced by Internet atmosphere should be further studied. Research in web atmosphere may help identify classes of cues that influence consumers' online affect (Eroglu et al. 2000).

In addition, replications using different types of websites are desirable. In this research, online auction stores are used as the stimuli because it provides both utilitarian and hedonic values and can potentially attract consumers with various purposes. Since different websites vary in terms of utilitarian value or hedonic value provided as the pretest suggested, types of websites may influence consumers' mood and the criterion that they may use to evaluate their performances.

Subjects' mood changed during/after the task is a phenomenon that needs further investigate. One explanation for the mood change could be that subjects used "mood management". According to Wegner and colleagues' hedonic contingency hypothesis (Wegener and Petty 1994; Wegener et al. 1995), whether people in different mood will process certain information in details depend on their anticipated consequences of information processing. When people anticipate that some activities might make them feel better, they will spend more time and increase message scrutiny. I suspect that such "anticipation" may weaken the hypothesized effects and lead to the mood improvement, especially for subjects in the negative mood. The analysis showed that when subjects whose mood changed significantly were screened out, the significance of the results was enhanced and the model fitted even better. Future research should address the issue of mood management as well as investigating whether this "anticipated mood improvement" will be exhibit when the mood is induced by and attributed to the website itself.

Finally, the current study used time spent on the task as the main dependent variable. Although variables such as time spent per store provided some evidence of consumers' information processing styles, other measures such as product recall, recognition, and evaluations should be incorporated in order to obtain a clear picture of processes of information processing. In addition, time spent in each store may be contaminated by the problem of caching.

In conclusion, the paper drew on the Affect-as-information hypothesis and tested how consumer affect and processing goals interact and influence online behaviors. Understanding how consumers behave provides marketers with insights on how to use the Internet more effectively. The model is discussed in the context of online behaviors. The Internet represents a very broad environment for various kinds of consumer activities so the model could be more applicable than the physical shopping environment. However, the model is generic and could be applied to other consumer shopping environments (e.g. conventional physical stores).

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