The role of online store atmospherics in consumer behavior

Transcription

1 AALTO SCHOOL OF ECONOMICS The role of online store atmospherics in consumer behavior Literature review Tommi Kaikkonen 31-May-12 The purpose of this paper is to examine the history of both offline and online store environment research, review the current discourse on online store atmospherics, see how it affects shopper behavior, and review empiric findings on atmospheric attributes.

2 1 Table of Contents and Introduction 1.1 Introduction Research questions The S-O-R paradigm Empirical research on the M-B model Motivational orientation Evolvement of the S-O-R model into the internet era S-O-R model by Manganari et al (2009) Store environment and atmospherics Online store environment The Online Store Environment Framework Virtual atmospherics Conclusion Theoretical framework Discussion References

3 1.1 Introduction Modern commerce first appeared in its modern form in early 13 th century Italy where a community of traders was active within a greater society. The political freedom was conducive to lively commerce and innovation. Similar to Italy s political context favored modern commerce, currently the context of globalization favors electronic commerce. (Heng, 2003) Electronic commerce has been on the rise especially since the invention of World Wide Web browsing in 1990 by Tim Berners-Lee. By the end of the 20 th century, online security had improved and continuous connection to the internet was possible via DSL (digital subscriber line), enabling e-commerce to function on a whole new level. In 2011, e-commerce and online retail sales in the United States alone were projected to reach $197 billion. 3/2012.) (Wikipedia, Defined broadly, electronic commerce includes any form of economic activity conducted via electronic connections (Wigand, 1997). Wigand also gave a narrower definition, where electronic commerce is defined as the seamless application of information and communication technology from its point of origin to its endpoint along the entire value chain of business processes conducted electronically and designed to enable the accomplishment of a business goal. Electronic commerce is more efficient than regular modern commerce in several ways. For example, labor and capital may be released from intermediary activities, such as a sales clerk, to the production of goods and services. (Heng, 2003). Electronic markets affect the consumer purchase decision in two ways. First, the digitization of market mechanism reduces customers search costs in terms of money, time and effort needed to gather information on price, quality and product features. Second, the information of products and their distribution is digitized. For the seller, digitization eliminates the need to maintain an inventory of goods (Turban et al., 2000, see Heng, 2003). These factors, combined 2

4 with a minimum need for personnel, have made electronic commerce a market with a low barrier to entry, which has resulted in high competition in price. In this literature review, I focus on one method of conducting electronic commerce: an online store, accessible with a computer through a browser via the internet. While I could not find an academic definition for online store, it was defined by emarketingdictionary.com (2009) as a web site that enables visitors to find, order and pay for products and services. Online store atmospherics have been studied widely (Eroglu et al., 2001; Sautter et al., 2004; Fiore and Kelly, 2007; Manganari et al. 2009; Vrechopoulos, 2010;). Furthermore, the way web designers establish atmospherics online is continually changing and evolving with leaps in web technology, such as CSS3 (Cascading Style Sheets), which improves upon web s layout and visual capabilities (see Figure 1). However, most of the academic releases on the subject do not address the technical level of establishing atmospherics. The study of online store atmospherics is gaining popularity as retailers are increasingly also etailers. Having an online store essentially expands the store s market to the whole world. Delivery systems, such as post offices enable e-tailers to ship goods globally. Establishing an online store is also getting easier, without the need for technical expertise increasing. One of the popular online commerce platforms, Shopify, offers a platform with payments, website hosting and website design as a complete service (Shopify, 2012). The purpose of this article is to examine the history of both offline and online store environment research, review the current discourse on online store atmospherics and how it effects customer behavior, and review empiric findings on atmospheric attributes, such as background music. 3

5 Figure 1: An example of evolution in web design. Web designers are implementing visual elements which create an illusion of depth and photorealism to websites: shadows, shading, gloss and more (The Evolution of Web Design, 2012) 1.2 Research questions The main research question is: How do online store atmospherics affect consumer behavior? In order to answer the main research question, I first answer two sub-questions: 1. How does the environment affect consumer behavior? In section 2, I review how environmental cues cause different consumer behavior according to the Stimuli-Organism-Response model. 2. What are online store atmospherics? Online store atmospherics is only one dimension of the online store environment. In order to fully understand online store atmospherics, I will explain the evolvement 4

6 of offline store environment and atmospherics research onto the internet era in section 3. Furthermore, I will observe the five online store atmospheric attributes with empirical findings on their application. Finally, in section 4 I will compile a theoretical framework to support my answer to the main research question and conclude my literature review. 2 The S-O-R paradigm The de facto way of connecting environmental cues and shopping outcomes is with the M-B (Mehrabian-Russell) model that uses the S-O-R paradigm (Donovan and Russell. 1982; Eroglu et al. 2001; Sautter et al. 2004; Manganari et al. 2009;). The S-O-R acronym comes from the words Stimuli Organism Response. The paradigm was adapted to environmental psychology by Mehrabian and Russell (1974; see Donovan and Russell, 1982). The model, shown below in Figure 2, has been the basis for many online and offline store atmosphere researches. Figure 2: The M-B model by Mehrabian and Russell (1974; see Donovan and Russell, 1982). The taxonomy of the Organism and Response sections are developed unlike the Stimuli section. This is because the sheer number of stimuli that is received from an environment should not be an obstacle to linking the stimuli to a response (Donovan and Russell, 1982). The later 5

7 adaptations of the model, especially in the online environment, have developed taxonomies for the environmental stimuli. Environmental stimuli produce an emotional state that can be characterized in three different dimensions: pleasure, arousal and dominance. Pleasure-displeasure refers to the degree to which the personal feels good, happy or satisfied in the situation. Arousal-nonarousal refers to the degree to which a person feels excited, stimulated, alert or active in the situation. The M-B model refers to subjective arousal, defined as subjective experience of energy mobilization (Russell and Feldman Barrett, 1999), while objective arousal is defined as the release of energy collected in the tissues. (Duffy 1962; see Kaltcheva and Weitz 2006) Dominance-submissiveness refers to the extent to which the individual feels in control of, or free to act in, the situation. (Mehrabian and Russel, 1974; see Donovan et al., 1982). The emotional states, which are pleasure, arousal and dominance, are uncorrelated, except for pleasure and arousal. Mehrabian and Russel (1974; see Donovan and Russell, 1982) propose that in a pleasant environment, high arousal leads to an approach response and an unpleasant environment with high arousal leads to an avoidance response. Similarly in a neutral environment, high and low arousal states lead to avoidance responses while moderate arousal leads to an approach response. In more recent applications of the model, the dominance dimension has been dropped. In a study by Russell et al. (1982), a lack of independence was found in the dimension, as it could be predicted from the other two dimensions. However, some researchers advocate its inclusion in online environments due to the difference between online and offline retail environments (Sautter et al., 2004). This is because in an offline setting, the retail environment is totally under the control of the store manager, though customers can openly voice their opinions. In an online environment, a customer might have the feeling that he has no control over the environment because he can t even communicate to staff directly. On the other hand, some websites give the user a chance to view the website in their preferred layout. 6

8 Moving on to the response part of the model, Mehrabian and Russell (1974; see Donovan and Russell, 1982) describe the four aspects of the approach and avoidance behaviors as: 1. A desire physically to stay in (approach) or to get out of (avoid) the environment 2. A desire or willingness to look around and to explore the environment (approach) versus a tendency to avoid moving through or interacting with the environment or a tendency to remain inanimate in the environment (avoidance) 3. A desire or willingness to communicate with others in the environment (approach) as opposed to a tendency to avoid interacting with others or to ignore communication attempts from others (avoidance) 4. The degree of enhancement (approach) or hindrance (avoidance) of performance and satisfaction with task performances. Donovan et al. (1982) adapted these behaviors to a retail environment, relating physical (1) behavior to store patronage intentions, exploratory (2) behavior to in-store search and exposure to retail offerings, communication behavior (3) to interaction with sales personnel and performance and satisfaction (4) to repeat-shopping frequency and reinforcement of time and money spent in the store. In summary, the M-B model predicts that people spend more time and possibly money in stores where they feel pleasant and from moderate to high arousal. 2.1 Empirical research on the M-B model In nearly all cases, pleasant shopping environments result in more approach behavior such as unplanned spending, duration of the store visit and social interaction (Kaltcheva and Weitz, 2006). Arousal effects of the environment are less inconsistent, resulting in both approach and avoidance responses. To find a missing variable that would explain the inconsistent results, Kaltcheva and Weitz (2006) conducted a study that hypothesized the shopper s motivational orientation to moderate the relationship between arousal and pleasantness (inside the organism-phase of the model). The results supported this. Task-oriented consumers wanted to complete their shopping as efficiently as possible, therefore finding high-arousal retail 7

9 environments unpleasant. Recreation-oriented consumers like high-arousal retail environments that create rich shopping experiences. 2.2 Motivational orientation In many recent adaptations, the M-B model has been updated with a motivational moderator. Usually the motivations are split into two orientations: utilitarian and hedonic. The utilitarian orientation involves consumers engaging in shopping out of necessity to obtain needed products, services or information with little or no inherent satisfaction derived from the shopping activity itself. The hedonic orientation describes consumers engaging in shopping to derive inherent satisfaction from the shopping activity itself. The shopping activity is freely chosen and there is no need to engage in it (Kaltcheva and Weitz, 2006). As an example, a customer with a utilitarian orientation might be looking for a screwdriver in a hardware store to fix a broken appliance, while a customer with a hedonic orientation might be on a vacation, shopping for the joy of it. In Kaltcheva and Weitz s model (2006), the motivational orientation is a moderator between arousal and pleasantness. While many new adaptations of the S-O-R model use some kind of a motivational orientation, there has been no consensus on where exactly it is positioned in the model. In the next section, where the S-O-R model is updated to the online age, we will see differing opinions of the motivational orientations position. 8

10 2.3 Evolvement of the S-O-R model into the internet era Figure 3: S-O-R model adapted by Eroglu et al. (2001) While Eroglu et al. (2001) brought the S-O-R model to the internet context as seen in Figure 3, Mummalaneni (2005) later validated the usefulness of the S-O-R model in understanding the relationships among website characteristics, emotional responses of shoppers and their purchasing behaviors. However, there is still no consensus on one optimal model for the online environment. Compared to the M-B model, the model by Eroglu et al. (2001) replaced Environmental Stimuli with Online Environmental Cues, which was divided into categories High Task Relevant and Low Task Relevant. High task relevant cues contain info related to the shopping goals, such as price, product description or return policies. Low task relevant cues do not affect the completion of the task, but may be used to create a pleasant atmosphere. The model also added Involvement and Atmospheric Responsiveness as moderators between stimuli and the organism. Involvement refers to the degree of personal relevance to the shopping goal. Atmospheric responsiveness is defined as the tendency to base patronage and purchase decisions on the stores physical design and condition (Grossbart et al, 1991). The cognitive state in the organism refers to everything that goes in the consumers minds concerning acquisition, processing, retention and retrieval of information. In an online store environment, it means how consumers interpret information provided on the screen, choose 9

11 from alternative sites and products as well as their attitudes toward the virtual stores. (Eroglu et al, 2001) Sautter et al. (2004) further adapted the Eroglu et al. (2001) model. Instead of task relevancy, their version categorizes the environment into two parts: the virtual store seen on a screen and the operator environment. The operating environment is the physical environment where the customer is. The internal state of telepresence is another addition to the organism of the model by Sautter et al. (2004), defined by Steuer (1992) as the extent to which one feels present in the mediated environment, rather than in the immediate physical environment. Telepresence is an important addition to the model as it explains why the most important environment during online store shopping is the virtual store and not the operator environment. Shopper motivation is also added as a moderator between the organism and the response; Sautter et al. propose that the optimal online store atmospherics are different for a consumer motivated by hedonism than by utility. Hedonism places importance on telepresence and affective states while utility puts emphasis on the efficient and effective delivery of wanted information and/or the completion of tasks. Their proposal was confirmed by Kaltcheva and Weitz (2006), as mentioned earlier, however they proved empirically that shopping motivation was in fact a moderator between pleasure and arousal rather than between the organism and the response. 10

12 2.3.1 S-O-R model by Manganari et al (2009) Figure 4: S-O-R model adapted by Manganari et al (2009) Evolved to a slightly different direction than by Sautter et al (2004), this model in Figure 4 by Manganari et al (2009) does not take the physical operator environment into account, rather it limits stimulus cues to those that are in the online environment. These cues will be thoroughly explained in Section 5, Store environment and atmospherics. Furthermore, while the model by Sautter et al (2004) puts shopping motivation as a moderating factor between organism and response, and Kaltcheva and Weitz (2006) has it as a moderator inside the affect between pleasure and arousal, this version by Manganari et al. (2009) has a similar but not identical motivational factor moderating between the stimulus and organism. The similarity can be seen as the Goal Attainment Orientation and Search Orientation grouping into the utilitarian function in shopping motivation and Experiental Orientation as the hedonic function. This is explained in Figure 5. 11

13 Figure 5: How both Sautter et al. (2004) and Manganari et al. (2009) implement motivational orientation to their S-O-R models, but in a different way. As I mentioned earlier, there is no consensus on how the consumers motivational orientation should be implemented into the S-O-R model. However, it seems that there is a consensus that shopper motivation should be included somewhere in the S-O-R model. Therefore, as a managerial implication, it is important to take that into account when designing an online store. 3 Store environment and atmospherics Now that we have looked at how environmental stimuli affect consumer behavior through internal states, we will have a look at how the discourse around store environments evolved to the web environment. Atmospherics, in the retail context, is the effort to design buying environments to produce specific emotional effects in the buyer that enhance his or her purchase probability. The atmospheres of individual surroundings can be described in sensory terms through sight, sound, scent, touch but not taste. Other artifacts in the atmosphere can however activate remembered tastes. (Kotler, 1973) For example, regular customers at a McDonalds restaurant will probably remember the taste of its fast food while walking by a restaurant through the scent of the food. Foxall (1997; see Falk et al., 2005) defines store atmospherics more specifically through the customers viewpoint: Store atmospherics are the means by which a consumption environment 12

14 creates emotional reactions in customers. Store atmospherics create attention and communicate both store image and level of service. Store atmospherics is a dimension of the store environment according to Lewison (1994; see Manganari et al., 2009), with other dimensions being store image and store theatrics. Lewison (1994; see Manganari et al., 2009) expanded on Kotler s atmosphere definition by building a taxonomy on the store environment seen below in Figure 6. Figure 6: Store environment by Lewison (1994; see Manganari et al., 2009) The first part of the store environment, store image, has quite a few definitions (Kunkel and Berry, 1968; Oxenfeldt, 1974; Zimmer and Golden, 1988; Berman and Evans, 1995; see Yoo and Chang, 2005). Berman and Evans (1995; see Yoo and Chang, 2005) defined it as a set of functional and emotional attributes that are organized in the perceptual structures of purchasers, and the structures are expectation on overall policies and executions of retailers. A much simpler definition comes from Martineau (1958), where store image is the way in which the store is defined in the shopper s mind. So to put simply, it means the different attitudes and beliefs, and other functional and emotional attributes that a consumer has on a store. Lewison s store atmospherics are similar to what Kotler (1973) proposed. With store theatrics, the environment is not a static one: it has special events (such as a celebrity appearance) or changing decoration (Lewison 1994; see Vrechopoulos and Siomkos, 2002). 13

15 3.1 Online store environment The Online Store Environment Framework Figure 7: the Online Store Environment Framework. Manganari et al (2009) Figure 7 shows the OSEF, or Online Store Environment Framework. Manganari et al (2009) extended Vrechopoulos et al (2004) initial OSEF with virtual social presence. Vrechopoulos s model was adapted to the online environment from Lewison s (1994). Virtual layout refers to the underlying web site structure (Griffith, 2005). In the offline world, where the three common layouts (grid, free-form and racetrack) originated from, their layouts are defined as (Vrechopoulos et al., 2004) the following: Grid is a rectangular arrangement of displays and long isles that generally parallel to each other. This is the standard in grocery stores. Freeform is free flowing with asymmetric arrangement, employing different sizes and styles of display. The freeform layout is common particularly in fashion stores. Racetrack is organized into individual areas built around a shopping theme. The customer is led through a predetermined path to visit as many section as possible. The racetrack layout is used in IKEA furniture stores. 14

16 Virtual theatrics, opposed to offline store theatrics (decór themes and store events), enables e- tailers to make their store look like a theatre through the use of images, graphics, animation and icons (Manganari et al., 2009). Virtual social presence is the implementation of social proof in an online environment. In an offline store, we can see other shoppers and employees; proof that other people shop at this outlet too. In an online store, the presence of other shoppers can be implied through web counters, comments and crowding (Eroglu et al., 2001) Virtual atmospherics Next I will define the attributes of virtual atmospherics and review empirical findings on them. I will not analyze scent appeal nor touch appeal, because they do not exist in an online setting but only in the operating environment. (general) Online store atmospherics may act as a visual primer that influences product choice (Mandel and Johnson, 2002) Background color Given that a website has a foreground (design elements, content) and a background (no content), background color is defined as the predominant color of the background. The background color is usually the most prominent color on a website, having the most screen area. There are quite a few classification systems for color, such as CMYK (Cyan, Magenta, Yellow and Key) for print and RGB (Red, Green, Blue) for light-emitting screens. HBS (Hue, Brightness and Saturation) is commonly used in academic papers due to the 3 dimensions being more intuitive. For example, a certain color of beige could be represented in RGB as 81% red, 76% green and 58% blue. When we express the color in HBS dimensions as 47 Hue (yellow), 81% Brightness and 28% Saturation, we can form a much clearer picture of the color in our heads. Some of the 15

17 older research in color theory can be methodologically flawed, as they don t control all the 3 dimensions of HBS (Valdez and Mehrabian, 1994). The effects of color have been studied widely, focusing on physiological and psychological impact (Bellizzi et al, 1983). On the physiological side, the color red has been found to have effects on people, such as heart- and respiratory rate increase, which are not conditioned (that is, we don t learn these effects; we are born with them) (Gerard, 1957; Clynes, 1977; see Bellizzi et al, 1983). On the other side of the color wavelength spectrum, blue decreases both heartand respiratory rate. On the psychological side, colors have been assigned different associations and emotional responses. Red is described as active, adventurous and stimulating (Bellizzi et al., 1983). Yellow is associated with cheer, gaiety and fun (Sharpe, 1974; see Bellizzi et al., 1983). Valdez and Mehrabian (1994) studied the effects of hue, brightness and saturation against the 3-dimensional PAD (Pleasure, Arousal Dominance) model. They found that as brightness and saturation increase, so does pleasure. Arousal and dominance increased linearly as saturation increased, but the effect of brightness was ladle-shaped: very dark and very bright colors increased both arousal and dominance. Hue was also found to affect pleasure: the short wavelength colors (such as blue) were the most pleasurable while green-yellow, yellow and yellow-red were the most unpleasurable. As we can see, the color blue is pleasurable and relaxing. It should be the safest choice when picking a background color or color scheme for an online store: compared to websites with a yellow background, people perceive that a website with a blue background has quicker download times (Gorn et al., 2004). People also have a more positive attitude towards the site and are more likely to recommend it to a friend. However the greatest effect on both relaxation and perceived quickness comes from brightness. In fact the most optimal color in terms of relaxation is one with a blue hue, low saturation (that is, more of a grayer blue) and high brightness (Gorn et al., 2004). For an example of the use of these colors, see Figure 8. 16

18 The website background may act as a visual primer that influences product choice (Mandel and Johnson, 2002). In Mandel and Johnson s (2002) study, the visual prime originated from a background image depicting different qualities of the available products, which resulted as a bias when choosing products to purchase. Figure 8: The Amazon online store uses low-saturation, high-brightness background colors and accentuates them with mostly blue and dark grey text a good example of a color scheme that strives for great relaxation and pleasure. (Amazon, 2012) Color scheme A color scheme is an arrangement or pattern of colors or colored objects conceived of as forming an integrated whole. (Dictionary.com, 4/2012) Adapted to an online context, the objects are the visual elements on a website. Color harmony is a term related to color scheme. While the exact definition of it is not agreed on, Burchett (2002) defines it as two or more colors that are brought together to produce a satisfying affective response. Therefore it is not enough to pick the right colors for an online 17

19 store based on, for example, the psychological associations of colors they should be in harmony. While no research has been done on the effects of unpleasant or even bad color harmony, one can only imagine the amount of avoidance behavior that could stem from it. Hall and Hanna (2004) studied the effect of different combinations of background and text color on readability. They found that while black on white has been often hypothesized as the optimal combination for readability because of its maximum contrast, other combinations matched it in legibility: white on black and light blue on blue. Based on these results, online stores have some leeway in the background/text color combination: colors can be used more freely to perhaps accentuate the store atmosphere without sacrificing readability Percentage of white space White space, or sometimes called negative space, is the open space found between other design elements or objects (Pracejus et al., 2006). Percentage of white space refers to the ratio of whitespace to design elements. White space is used in many types of documents, such as academic journals. There is whitespace in the margins, on each four sides; there is white space between the lines of text. It is due to a reason: it gives the readers a more satisfied reading experience, even though it doesn t affect our reading speed or comprehension (Bernard et al., 2000; Chaparro et al., 2004). Looking at other effects of white space, it has an impact on brand perceptions. Showing ads of different sizes and with different percentages of white space, Pracejus et al. (2006) found that white space is associated with higher quality, prestige, trust, leadership and lower risk. One can imagine that the natural instinct of an online store owner is to cram as many products into the shopper s view. That instinct is wrong: usability studies have consistently shown the value of white space as an aid to locating and understanding information (Redish, 2000) Background music Sound in an online store can be divided into two categories: content and atmospheric sound (Coorough, 2011; see Fiore and Kelly, 2007). Content refers to, for example, a sound clip of a 18

20 music CD to give the consumer a chance to listen to the music before buying. Background music can be thought of as an equivalent for atmospheric sound. Atmospheric sound is not very common in online stores. While not generalizable, a study conducted about sound in 70 online stores found that only two had atmospheric sound (Fiore and Kelly, 2007). They recommend future research on the subject, as most web atmospherics studies are centered on visual aspects. Background music can have an effect on product choice. A study conducted in an online wine store (note: not peer reviewed) showed that more customers chose wine of the same geographical origin as the background music, given that they recognized the music s origin (Beukeboom et al, 2009) Font Font is defined as the glyphs that make up a typeface that is rendered on screen to produce text. World Wide Web had been restricted to only several core fonts, but today with CSS3 (Cascading Style Sheet 3, a browser-technology to implement styling to websites), any font can be embedded on a website (Smashing Magazine, 2009). As this increases the number of usable fonts online, more attention should be paid to the selection of the typeface. People assign personalities to typefaces, such as elegance, directness and friendliness (Brumberger, 2002), therefore the typeface personality should be chosen to be in line with the target store image. However, many websites still opt to use web safe fonts because of the latency caused by user downloading a different font (usually several hundred milliseconds) and their optimization for screen reading. This can make a difference in countries where connection speed is low, as the tolerable waiting time for internet users is approximately 2 seconds (Nah, 2004). If that time is exceeded, they might abandon the site. Some people have proposed that sans-serif fonts make for better reading performance on screen rather than serif fonts (Davidov, 2002; see Ling and Schaik, 2006). Bernard et al. (2003) found that the sans-serif font Arial was preferred to the serif font Times, even though there was 19

21 no difference in reading performance. Ling and van Schaik (2006) also came to the same conclusion. It should be noted that computer screen technology is improving continually and getting closer to the visual accuracy of print, so these findings might become less relevant in the future. 4 Conclusion 4.1 Theoretical framework Figure 9. A theoretical framework that shows how the online store atmospherics discussed in this paper affect consumer behavior. Adapted from Eroglu et al. (2001), Sautter et al. (2004) and Kaltcheva and Weitz (2006) I have assembled together a theoretical framework in figure 9 that holds the answer to this literature review s research question, how do online store atmospherics affect consumer behavior? The framework is linked especially to online store atmospherics (the context of this paper) while including the most applicable modules found in the newer adaptations of the M-B (Mehrabian-Russell) model, such as atmospheric responsiveness, telepresence and shopper motivation. I will now go through the framework, using the information from previous sections. Online store atmospherics affecting consumer behavior is a process; and that process is depicted with the S-O-R model, which is the de facto way of linking the online store 20

22 environment to consumer behavior and has been proven empirically by Mummalaneni (2005). The first part of the three-part process is stimulus, in which I have limited the environmental cues to online store atmospherics for the purposes of this paper. Operator environment is also a part of stimulus; it indicates that besides the online environment, the user is in another environment altogether. However I have not listed any cues below the operator environment. This is because of telepresence inside the organism. Telepresence is the extent to which the user feels present, in this case, in the online environment rather than in the immediate physical environment (Steuer, 1992). Atmospheric responsiveness is a mediator between stimulus and organism. It is defined as the tendency to base patronage and purchase decisions on the stores physical design and condition (Grossbart et al., 1990). In the context of this paper, the atmospheric responsiveness of the consumer has the ability to either amplify or reduce the effect of atmospheric cues on the consumers internal states. The next part of the process, organism, includes affect, or emotional states, which is tied closely with cognition (Forgas, 2008). The cognitive state refers to what goes on in the consumer s minds concerning acquisition, processing, retention and retrieval of information (Eroglu et al., 2001). The emotional states can be rendered through the 3-dimensional PAD (Pleasure-Arousal-Dominance) model. I have added shopper motivation as a mediator between pleasure and arousal as proposed and empirically supported by Kaltcheva and Weitz (2006). The shopper motivation includes the motivational orientations of utilitarian and hedonic type. The last part of the process is response. This is the part where actual behavior happens, categorized either as an approach or avoidance response. 4.2 Discussion Most of the info and know-how in online atmospheric attributes lies behind professional designers applying best practices. They can design online stores that are pleasurable to look at due to color harmony, intelligent use of white space and great typefaces. Most academic research focuses on simple and foundational elements, such as the psychological effects of 21

23 different colors or the combination of background and text color. They do provide a solid knowledge base to start building an online store on, but establishing great atmospherics also requires aesthetics. What I mean is, an aesthetic experience is required such as one experienced with great color harmony. There is no exact science for this as views, for example on color combinations, can be very subjective. A manager should inform the designer about the target audience of the online store, and hope that he can create an aesthetic experience to enhance the atmospherics for that audience. Another important aspect to take into account is the motivational orientation of the shopper. An online hardware store should be designed in a different fashion than an online store for luxury watches. This literature review makes it clear that atmospherics have a significant influence on consumer behavior in the online setting. Online store atmospherics can influence which product a consumer chooses to buy (Beukeboom et al., 2009), they can affect store image through different atmospheric attributes such as font personality (Brumberger, 2002) or amount of white space (Pracejus et al., 2006). Colors and color schemes affect us both physiologically and psychologically (Bellizzi et al., 1983) and those effects should be taken into account when managers plan for an online store. Furthermore, as we look at all the atmospheric attributes as a whole, their congruency can enhance the effect on consumer s emotions (Cheng et al., 2009). For example, fast paced music coupled with a yellow-red background color will make for high arousal atmospherics, while slow paced music coupled with a cool blue background color will make for low arousal atmospherics. Because of the high competition online store owners face, especially in the price category, e- tailers should look for different ways to differentiate themselves. Atmospherics is one of these ways. It can make people recommend the website on their friends (Gorn et al., 2004), which could make the difference between growing or declining profits. 22

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