Canonical correlation analysis of online video advertising viewing motivations and access characteristics

Transcription

1 NMS / Lee and LeeNew Media & Society Article Canonical correlation analysis of online video advertising viewing motivations and access characteristics new media & society 14(8) The Author(s) 2012 Reprints and permission: sagepub.co.uk/journalspermissions.nav DOI: / nms.sagepub.com Joonghwa Lee Middle Tennessee State University, USA Hyunmin Lee Saint Louis University, USA Abstract This study investigates consumers motivations for watching online video ads and the relationship between the motivations and access characteristics of viewers. Uses and gratifications and characteristics of consumers exposure to online video ads were reviewed for this study. First, findings of exploratory factor analysis revealed five different motivations for viewing online video ads social interaction, relaxation, information, escapism-pass time, and entertainment. Second, canonical correlation analysis revealed that the desire to fulfill viewing motivations is positively correlated with frequencies to actively access websites and frequencies to visit different types of websites. Implications and future research are discussed. Keywords audience activity, canonical correlation, motivation, online video ads, uses and gratifications Introduction Video advertising has traditionally focused on television as the platform to reach consumers and entice them to buy products or services presented in ads. Television Corresponding author: Hyunmin Lee, Department of Communication, Saint Louis University, 3733 West Pine Mall, Saint Louis, MO 63108, USA

2 Lee and Lee 1359 ensures mass exposure, and the forced nature of viewing (i.e. people cannot control the content from appearing on television) suggests television is better than any other medium at effectively playing video (Dijkstra et al., 2005). However, such underlying assumptions of television advertising are on the verge of facing serious theoretical challenges. According to emarketer (2009), TV viewership is dropping and viewers are now fastforwarding through commercials with their digital video recorders (DVRs). This trend is reflected in advertisers online video ad spending. US online video ad spending was $734 million in 2008, and it is expected to grow to $5.52 billion in 2014 (emarketer, 2010). In sum, video is the fastest-growing content format on the internet, and advertisers need to find a way to redefine their businesses and take advantage of this new environment. Online video ads (OVAs) combine the branding power of traditional broadcast advertising with the direct response power of the Web, creating advertising content that is more compelling to consumers than other online advertising forms such as banner ads or pop-ups (emarketer, 2010; Southgate et al., 2010). It is also cost-effective: with the right tools, even individual contributors can create and broadcast high-quality video ads through social media websites such as YouTube (Business Week, 2009). While there has been an explosion in OVA growth rates, much is left unknown about how consumers actually use OVAs. Why is it that people are willing to watch video ads online when they cannot wait for commercial breaks to be over on television? The present study seeks to investigate what motivates consumers to watch OVAs, to examine the relationship between the set of identified motivations and access modes of viewers, and to examine the relationship between motivation sets and the types of websites viewers visit in order to watch OVAs. Findings of this research will benefit online advertising researchers by providing practical implications to understand the dynamics of viewer motivations and understand how their target consumers access OVAs. Literature review Online video ads defined. OVAs typically belong to one of two groups: (1) OVAs with playback control functions (e.g. buttons of play, pause, fast-forward, etc.) that are usually independently uploaded on websites, and (2) OVAs without playback control functions that are usually embedded in other online video clips (Lee and Lee, 2008). While OVAs are similar to traditional TV commercials, the unique video playback controls allow consumers to control their exposure to the advertising content. Unlike the forced exposure of traditional TV commercials (Dijkstra et al., 2005), consumers are voluntarily exposed to OVAs when they search for a specific ad or product (Lee and Lee, 2011). Such voluntary exposure to OVAs may have significant implications because it empowers consumers and entices active participation. In this sense, it is meaningful to understand why consumers are voluntarily exposed to OVAs, how they access these ads, and where they view these ads. We apply the theoretical framework of uses and gratifications (U&G) to understand the motivations for viewing OVAs. Uses and gratifications. The uses and gratifications (U&G) perspective has been widely applied to understand people s motivations for selecting a particular medium. The theory assumes that when people use media, they have specific goals and motivations to choose

3 1360 new media & society 14(8) the media that will satisfy their needs (Katz, 1959). Another assumption is that the media environment is competitive within each medium to gratify people s needs (Katz et al., 1974). Based on these assumptions, the perspective between media and people advanced from what media do to people to what people do with the media (Katz, 1959). U&G has brought questions about why people seek particular media and how people use those media, acknowledging that media users control their own decisions (Lin, 1999). The perspective has been applied to understand consumers use of online media such as the audiences experiences with websites (LaRose and Eastin, 2004; Papacharissi and Rubin, 2000), online purchasing motives (Korgaonkar and Wolin, 1999), use of the internet for political information (Kaye and Johnson, 2002), online community uses (Grace-Farfaglia et al., 2006), cultural differences in internet usage motivations and perceived interactivity (Ko et al., 2006), playing games online (Chang et al., 2006; Youn and Lee, 2005), content generation online (Leung, 2009), and electronic book uses (Shin, 2011). The uses and the gratifications are the two core components of U&G, and each component has its own implications for using media. The uses component indicates both duration of media exposure and particular media type or media choice (Lin, 1999). In the passive audience perspective, audiences are considered targets by means of one-way communication from media to audiences; however, from the U&G perspective, audiences are considered active participants who can choose media they want to use (Leung, 2009; Shin, 2011). Thus, before they are exposed to media, people can plan media choices according to their preference and purpose, which in turn makes the audience more involved in the media (Lin, 1999). The gratifications component refers to the type and level of how audiences can fulfill their cognitive and emotional needs through the range of media use (Lin, 1999; Shin, 2011). There are two sub-concepts in gratifications: gratifications sought and gratifications obtained (Dobos, 1992; Dobos and Dimmick, 1988; Lin, 1999; Palmgreen et al., 1980). Gratifications sought refer to the needs consumers anticipate from media usage, such as needs, expectations, and motivations for using media; gratifications obtained refer to the ways users feel satisfied and fulfilled through particular media use (Dobos, 1992; Lin, 1999). Gratifications sought affect the audience s level of satisfaction about gratifications obtained. In turn, gratifications obtained direct future motivations (i.e. gratifications sought) for using media (Lin, 1999). Although numerous studies have employed U&G in exploring the use of both traditional and new media, few studies have applied the perspective in advertising consumption. This may be attributed to the nature of traditional advertisement viewing which forces exposure to the content, making viewers passive recipients of content rather than active seekers; however, OVAs with video playback controls allow consumers to actively participate in selecting and watching the ads. Therefore, it is appropriate to apply U&G to understand consumers motivations for watching OVAs. Motivation for viewing advertising and using the internet. Motivations for viewing advertising are broadly classified into six different dimensions (O Donohoe, 1994): marketing uses (e.g. using advertising to search for product information); structuring time (e.g. using advertising breaks for doing something other than watching the advertisement); enjoyment (e.g. using advertising for entertainment); scanning the environment (e.g.

4 Lee and Lee 1361 using advertising for monitoring trends); social interaction (e.g. using advertising as an agenda for conversation); and self-affirmation/transformation (e.g. using advertising to reinforce attitudes and values). Additionally, it is important to consider consumers uses of the internet, as it is the fundamental platform for placing online video ads. Stafford and Stafford (2001) identified five motivations for using e-commerce websites: using the internet as a tool for the latest updates and resources; means of information seeking; searching for new and unique ideas and interests; fulfilling social interactions; and entertainment needs. Korgaonkar and Wolin (1999) also developed seven motivations of Web usage: escapism, information, socialization, interactive control (e.g. choosing websites), transaction-based security and privacy concerns, non-transactional privacy concerns, and economic motivation for shopping and buying. Finally, most relevant to the current study, Lee and Lee (2011) conducted an exploratory study using focus groups and survey data to investigate why consumers watch OVAs, and found that consumers want to fulfill entertainment, relaxation, escapism, pass time, social interaction, and control gratifications. Consumers exposure to online video ads. OVAs are an independent form of video advertising, which is delivered online with control functions. User control and audience activity related to OVA consumption are demonstrated in three phases: pre-exposure, duringexposure, and post-exposure activities (Lin, 1999; Niekamp, 2003). Each phase shows characteristics of user control as well as audience activity. Pre-exposure activity reflects audiences preparation such as reading media schedules and searching multiple channels (Lin, 1999; Niekamp, 2003). Active consumers are both active message seekers and message recipients (Pavlou and Stewart, 2000). At the pre-exposure stage, consumers actively decide whether they will visit a certain website and click on certain OVAs or not, making the experience voluntary rather than forced (Lee and Lee, 2011). Cho (1999) explains that voluntary exposures to ads such as visiting websites and clicking video clips may result in increased attention to the contents and stimulate the cognitive learning process. During-exposure activity is the degree of audience involvement with the content and media (Lin, 1999). With OVAs, consumers are actively engaged in the message process using playback control buttons. Furthermore, when consumers judge OVAs interesting or meaningful, they can further engage by watching them repeatedly at one sitting (emarketer, 2010). Previous researchers have suggested that interactive functions of the internet such as the capability of selecting and avoiding presented messages make consumers process information more actively (Rodgers and Thorson, 2000; Sicilia et al., 2005; Widing and Talarzyk, 1993) and have positive attitudinal outcomes towards the product (Ariely, 2000). Post-exposure activity includes short-term and long-term effects of the media exposure such as talking with others about the content, feeling positive emotions such as joy or amazement, or having positive attitudes about the item or topic advertised (Lin, 1999; Niekamp, 2003). After watching OVAs, consumers can post their opinions or rate the ads on websites. Additionally, OVAs with video playback controls usually include the option to share, which helps consumers pass along the ads to their friends or family via

5 1362 new media & society 14(8) or social media channels when desired (Cruz and Fill, 2008; Eckler and Bolls, 2011; Norman and Russell, 2006). Voluntary exposure and control over the presentation of messages usually go hand in hand. Pavlou and Stewart (2000) mentioned that interactive advertising formats used on websites challenge traditional advertising assumptions because consumers actively determine what they watch. Given that exposure to and viewing of OVAs with video playback controls require active participation on the part of the consumer, it is interesting to examine why consumers are voluntarily exposed to OVAs. Derived from the review of U&G and the unique characteristics of OVAs, the first research question is proposed: RQ1: What motivates consumers to watch OVAs? Audience activity and watching online video ads. The most prominent and important concept of U&G contends that people consume media for their own purposes and activity interests (Leung, 2009). Audience activity is a cognitive, affective, and behavioral process (Lin, 1999) of how intentionally and purposely people select, attend to, and use media and their content (Perse, 1990: 676). The main point of audience activity is that audiences select media that are satisfying to achieve their needs based on their beliefs and feelings (Palmgreen, 1984). They engage in media exposure by their prior learned experiences before and after watching behaviors, or recalls after exposure (Levy and Windahl, 1984; Niekamp, 2003). With OVAs, consumers are active audiences in that they control what they watch and how long they watch by visiting websites, clicking on OVAs, and controlling the exposure length with playback functions (Lee and Lee, 2011). However, although consumer activity is required for watching OVAs, there are different degrees of access to them. That is, consumer activeness may differentiate according to the varying levels of access mode, which refers to how different users are exposed to OVAs. For example, more active users may personally seek information while passive users may encounter OVAs by chance. In this sense, access mode is an interesting variable as it indicates the level of activeness of the viewer. Thus, the second research question asks: RQ2: What types of relationships are there between the set of identified motivations for viewing OVAs and the set of users access modes in watching OVAs? Website types and online video watching motivations. Another meaningful factor is the type of the website on which people view OVAs. According to the Pew Internet & American Life Project Tracking Survey (2007), 27% of online video consumers said that they watch or download video from YouTube. The same survey also reported that young adults are almost twice as likely to point to YouTube as a source for online video. The survey also revealed that news websites were the second most popular place to view online videos, followed by cable or network TV sites (12%), portal websites (9%), social networking sites (6%), and so forth (PEW, 2007). Identifying the relationship between motivations for viewing OVAs and the websites people use to view them has many

6 Lee and Lee 1363 practical implications. Thus, this study asks the relationship between the set of identified motivations for viewing OVAs and the set of different types of website: RQ3: What types of relationships are there between the identified set of motivations of viewing OVAs and the set of different types of websites for watching OVAs? Methods Exploratory factor analysis (EFA) was conducted to identify motivations for viewing OVAs. Additionally, canonical correlation was conducted to assess the relationship between the set of identified motivations from the EFA and: (1) the set of access modes of OVAs, and (2) the type of website used to watch OVAs. Participants Three hundred and thirty-two undergraduate students at a Midwestern university were recruited to examine the proposed research questions. 1 Participants were compensated for their time with extra credit in their courses. After being informed of their rights as study participants, the students were asked to indicate their opinions and behaviors with respect to OVAs by answering a series of questions. Questions about the access modes directing users to OVAs and the types of websites the participants accessed to watch them were also asked. Data screening Data screening for missing values, univariate outliers, and multivariate outliers was conducted prior to running statistical analyses. From the total number of participants (n = 322), 7 univariate outliers and 26 multivariate outliers were deleted, making the total sample size Measures To identify the motivations for watching OVAs, the researchers developed their survey based on the questionnaire construction guidelines by Ajzen (2002/2006) reflecting the expectancy value model (Fishbein and Ajzen, 1975). The questionnaire construction was modified according to Lee and Lee s (2008) motivation study. Multiple-item scales were used for all variables in this measure. Motivations for watching OVAs. Behavioral beliefs refer to the subjective probability that the behavior will achieve outcomes that consumers expect to accomplish when watching OVAs (Fishbein and Ajzen, 1975). Five decomposed behavioral beliefs of watching advertising and using the internet (i.e. social interaction, information, relaxation, escapismpass time, and entertainment) and the strength of each behavioral belief and its corresponding outcome evaluation were measured to identify motivations for viewing OVAs.

7 1364 new media & society 14(8) The strength of social interaction as a behavioral belief (e.g. Watching OVAs will help me to talk with others about OVAs) and its outcome evaluation (e.g. For me to watch OVAs is to talk with others about them is ) were measured by six items borrowed from Ferguson and Perse (2000). The strength of information (e.g. Watching online video ads will help me to obtain information easily) and its outcome evaluation (e.g. For me to watch online video ads to obtain information easily is ) was measured by five items (Kaye and Johnson, 2002; Ko et al., 2005; Korgaonkar and Wolin, 1999; Papacharissi and Rubin, 2000). Five items were adjusted to fit to the context of OVA to measure the strength of relaxation (e.g. Watching OVAs will help me to unwind) and its outcome evaluation (e.g. For me to watch OVAs to unwind is ). The strength of escapism-pass time (e.g. Watching OVAs will give me something to occupy my time) and its outcome evaluation (e.g. For me to watch OVAs to occupy my time is ) were also measured by five items modified from previous studies (Ferguson and Perse, 2000; Papacharissi and Rubin, 2000). Finally, the strength of behavioral belief of entertainment (e.g. Watching OVAs will help me to be amused) and its outcome evaluation (e.g. For me to watch OVAs to be amused is ) were measured by two items (Ferguson and Perse, 2000). The strength of each behavioral belief was assessed on a seven-point scale ranging from extremely unlikely (1) to extremely likely (7). The outcome evaluation of each expected outcome was measured on a seven-point scale ranging from extremely bad (1) to extremely good (7). To compute the decomposed behavioral beliefs, the strength of each behavioral belief was multiplied by its corresponding outcome evaluation, and the resulting products were summed for each outcome factor (i.e. b i e i for each outcome factor). Access mode. Access mode for OVAs was measured using a seven-point scale ranging from never (1) to always (7) as a set. Four items were measured for access mode: I access OVAs by actively searching for specific ads on the Internet_, by clicking links in s my friends sent to me_, by clicking ads posted on friends social networking websites_, and by browsing websites accidentally_. Type of website. The types of websites participants use to watch OVAs were also measured using a seven-point scale ranging from never (1) to always (7) as a set. Five items were used for the type of website: I view OVAs on video sharing websites_, social networking websites_, brand websites_, portal/news websites_, and others. Results The age of the participants ranged from 18 to 26 years old, with a mean age near 21 (20.6). Female participants (n = 185, 64%) outnumbered male participants (n = 104, 36%). Of the 289 participants, more than 270 (93.6%) watch OVAs at least once a month: 40.5% (n = 117) of the participants reported watching at least one a week, while approximately 8% (n = 23) of respondents watched OVAs at least once a day. The participants most frequently watch OVAs on video-sharing websites (M = 4.72, sevenpoint scale), followed by social networking websites (M = 3.56), portal/news websites (M = 2.87), and brand websites (M = 2.52), respectively. Additionally, the participants accessed OVAs by actively searching for specific ads on the internet (M = 3.99, sevenpoint scale), followed by clicking links in s their friends sent to them (M = 3.92),

8 Lee and Lee 1365 by browsing websites accidentally (M = 3.57), and by clicking ads posted on friends social networking websites (M = 3.46), respectively. Finally, the participants relatively showed positive attitudes toward OVAs (M = 5.10, seven-point scale). RQ1: Motivations for watching online video ads (OVAs) Motivations for watching OVAs were analyzed using EFA using the principal axis factoring and the orthogonal rotation with varimax rotation and run over 25 computed items. Based on previous literature of the motivations for watching OVAs, five factors were extracted to group behavior items into different factors. In order to select items to be included in each factor, two criteria were cross-referenced: (1) a cut point of factor loading of.40 or higher, and (2) no significant cross loading. However, a factor loading of.30 was also assessed to gain more insight on determining the variables placement for each factor. Two computed items were deleted from the matrix as the difference was less than.15 ( Watching OVAs will help me to find unique ads and it is good, Watching OVAs will help me to enjoy and it is good ) and cross-loaded ( Watching OVAs will help me to enjoy and it is good ). This analysis generated five factors that accounted for % of the total variance: social interaction, information, relaxation, escapism-pass time, and entertainment. While the general rule of thumb requires that all factors have more than three variables with loadings greater than.45 (Tabachnick and Fidell, 2007), the entertainment factor was kept as a motivation for viewing OVAs as the loadings for the two variables were over.50, and previous literature emphasizes that one of the most important motivations for viewing advertisements is for entertainment purposes (e.g. O Donohoe, 1994). Each factor had an eigenvalue above 1.0. Cronbach alphas of all five factors ranged from.87 to.94. Table 1 illustrates specific findings of the EFA. The first factor, labeled social interaction, accounted for 18.6% of the variance of six items (α =.94). Social interaction as a behavioral belief reflects the subjective probability that watching OVAs would provide interpersonal gratifications from communicating, sharing or discussing issues of the ads with others, which help consumers build or enhance social relationships. The second factor, labeled relaxation, consisted of five items and explained 17.62% of the variance (α =.92). This factor represents the subjective probability that using OVAs will help consumers refresh and take a break. The third factor, labeled information accounted for 15.59% of the variance with five items (α =.91). This factor delineates the subjective probability that watching OVAs will help consumers obtain necessary information. The fourth factor, named escapism-pass time, accounted for 14.69% of the variance with five items (α =.92). This factor refers to the subjective probability that watching OVAs will allow consumers to get away from work and boredom. The final factor, labeled entertainment, accounted for 5.1% of the variance with two items (α =.87). This factor refers to the subjective probability that watching OVAs will help consumers feel amusement and excitement. RQ2: Relationships between motivation set and access modes set The second research question asked if there was a relationship between the identified motivations for watching OVAs and the set of access mode for viewing OVAs. Canonical

9 1366 new media & society 14(8) Table 1. EFA results of behavioral beliefs for watching online video advertising. Behavioral Belief Items b i e i Factor loading % of variance Alpha Watching online video ads will Factor 1: Social Interaction b1e1. Give me an opportunity to watch the online video ads that my friends tell me about and it is good b2e2. Help me to talk to my friends about online video ads and it is good b3e3. Help me to talk with others about the online video ads I find and it is good b4e4. Help me to tell people about the online video ads I like and it is good b5e5. Help me to watch ads that created buzz around and it is good b6e6. Help me to watch specific ads that my friends send me links to and it is good Factor 2: Relaxation b7e7. Help me to unwind and it is good b8e8. Help me to pep me up and it is good b9e9. Help me to relax and it is good b10e10. Help me to have a pleasant rest and it is good b11e11. Help me to be thrilled and it is good Factor 3: Information b12e12. Help me to learn about unknown things and it is good b13e13. Help me to get information for free and it is good b14e14. Help me to obtain information easily and it is good b15e15. Help me to learn about things happening in the world and it is good b16e16. Help me to learn about useful things and it is good Factor 4: Escapism-Pass time b17e17. Help me to forget about school, work or other things and it is good b18e18. Give me an opportunity to do something when I have nothing better to do and it is good b19e19. Give me something to occupy my time and it is good b20e20. Help me to get away from what I m doing and it is good b21e21. Help me to pass the time away, particularly when I am bored and it is good Factor 5: Entertainment b22e22. Help me to be amused and it is good b23e23. Help me to be entertained and it is good Cumulative % of variance

10 Lee and Lee 1367 correlation test for motivations for watching OVAs and access modes revealed that two canonical correlations are significant. 3 Using the cutoff correlation of.3 to select variables for each variable set, the variables in the set of motivation for watching OVAs that were correlated with the first canonical variate were social interaction, information, relaxation, entertainment, and escapism-pass time. Among the access mode variables, active searching, links sent from a friend s , clicking ads from a friend s social networking website, and accidental browsing were correlated with the first canonical variate. The first pair of canonical variates reveal that viewing OVAs with motivations for social interaction (correlation =.913, coefficient =.645), information (correlation =.620, coefficient =.144), relaxation (correlation =.838, coefficient =.352), entertainment (correlation =.729, coefficient =.023), and escapism-pass time (correlation =.787, coefficient =.240) are associated with actively searching information on websites (correlation =.762, coefficient =.539), clicking on links sent from friend s (correlation =.673, coefficient =.098), viewing on ads from a friend s social networking site such as MySpace or Facebook (correlation =.767, coefficient =.395), and accidently viewing online advertising (correlation =.598, coefficient =.368). The second canonical variate related to motivation included motivation for watching OVAs for entertainment, and the corresponding canonical variate from the access mode set included active searching and accidentally browsing websites. Taken as a pair, these variates suggest that the viewing of online advertising motivated by entertainment needs (correlation =.587, coefficient = 1.271) is positively associated with a combination of more active searching of websites (correlation =.576, coefficient =.624), while it is negatively associated with the combination of accidental browsing of websites (correlation =.606, coefficient =.584). Table 2 summarizes the findings. Table 2. Correlations, standardized canonical coefficients, canonical correlations, percents of variance explained, and redundancies between motivation and access variables and their corresponding canonical variates. First Canonical Variable Second Canonical Variable Correlation Coefficient Correlation Coefficient Motivation set Social Interaction Information Relaxation Entertainment Escapism-pass time Percent of variance Redundancy Access mode set Active searching Links from friends Ads from friend s website Accidental browsing Percent of variance Redundancy Canonical correlation Total =.710 Total = Total =.709 Total =.140

11 1368 new media & society 14(8) RQ3: Relationships between motivation sets and website types set The second canonical correlation test, motivation for watching OVAs and types of website for viewing OVAs, also revealed that two canonical correlations are significant. 4 Using the cutoff correlation of.3 to select variables for each variable set, the variables in the motivation set that were correlated with the first canonical variate were social interaction, information, relaxation, entertainment, and escapism-pass time. Among the website type variables, video-sharing websites, social networking websites, brand websites, and portal/news websites were correlated with the first canonical variate. The first pair of canonical variates reveal that viewing OVAs with motivations for social interaction (correlation =.876, coefficient =.559), information (correlation =.647, coefficient =.146), relaxation (correlation =.908, coefficient =.671), entertainment (correlation =.735, coefficient =.058), and escapism-pass time (correlation =.673, coefficient =.070) are associated with viewing online advertising on video-sharing websites (correlation =.771, coefficient =.538), social networking websites (correlation =.684, coefficient =.238), brand websites (correlation =.657, coefficient =.323), and portal/news websites (correlation =.891, coefficient =.305). The second canonical variate related to motivation included motivation for watching OVAs for entertainment and escapism-pass time, and the corresponding canonical variate from the website type set included video-sharing websites, social networking websites, and brand websites. The second pair of canonical variates reveal that viewing OVAs with motivations for entertainment (correlation =.476, coefficient = 1.133), and escapism-pass time (correlation =.423, coefficient =.829) are associated with viewing online advertising on video-sharing websites (correlation =.539, coefficient =.875), social networking websites (correlation =.492, coefficient =.766), and brand websites (correlation =.413, coefficient =.426). Table 3 summarizes the findings. Discussion This study attempted to provide insights into the relatively unknown nature of OVAs, specifically in terms of viewing motivations and how these motivations relate to the access mode of viewers and the type of websites viewers use to watch these advertisements. From the findings of the exploratory factor analysis, five different motivations for viewing OVAs social interaction, relaxation, information, escapism-pass time, and entertainment were identified. These findings are interesting as they produce results similar to previous motivation studies of internet usage and advertising viewing (e.g. Ferguson and Perse, 2000; Kaye and Johnson, 2002; LaRose and Eastin, 2004; Lee and Lee, 2008, 2011; Papacharissi and Rubin, 2000; Stafford and Stafford, 2001). To recap the findings, social interaction motivation reflects that people watch OVAs in order to build or enhance social relationships. They may have social pressure from reference groups about watching OVAs or they may share their opinions about the OVAs they watch. Relaxation motivation implies the desire to refresh oneself mentally and take a break from a hectic lifestyle. Information motivation reflects the need for valuable, easily accessible, and free information in OVAs. Escapism-pass time motivation implies

12 Lee and Lee 1369 Table 3. Correlations, standardized canonical coefficients, canonical correlations, percents of variance explained, and redundancies between motivation and types of website and their corresponding canonical variates. First Canonical Variable Second Canonical Variable Correlation Coefficient Correlation Coefficient Motivation set Social interaction Information Relaxation Entertainment Escapism-pass time Percent of variance Redundancy Type of website Video sharing Social networking Brand websites Portal/News websites Percent of variance Redundancy Canonical correlation Total =.696 Total = Total =.673 Total =.133 the desire to get away from daily routines, work, or boredom while entertainment motivation refers to the need for amusement and excitement. These motivations may be grouped once again in terms of independent needs or social needs. That is, motivations such as relaxation or escapism-pass time may fit into independent needs while social interaction may fit into the social needs. This knowledge provides advertisers and marketers with insights on how to target their consumers using OVAs either on an individual level or social level. Thus, to influence an individual consumer, advertisers may create OVAs focusing on independent needs (e.g. relaxation or escapism-pass time); however, for group-based consumers, they may create OVAs including social motivation cues. In sum, advertisers and marketers interested in using OVAs should carefully structure the advertisement to meet the varying motivational needs of the consumer, while simultaneously being mindful of the matching gratification needs. Another focus of this study was to identify the relationships between the set of motivations and the access mode of viewing OVAs. Findings from the canonical correlation analysis revealed that the higher the desire for social relaxation, information, escapism-pass time, and entertainment, the higher the frequency of actively accessing websites, clicking on links sent from a friend s s, clicking on ads from a friend s website, and accidental browsing. Additionally, those with high motivation for escapismpass time are more likely to actively search websites while they are less likely to encounter OVAs by chance. Such findings suggest that even the audiences of OVAs differentiate in terms of the degree of activeness. Although many scholars agree that viewers of OVAs are already active because they have voluntary exposure to the content

13 1370 new media & society 14(8) (Lee and Lee, 2008, 2011; Palmgreen, 1984; Perse, 1990), we can investigate this concept further and identify the dimension of activeness based on their access mode. From a marketing perspective, it is important to understand that there are different levels of audience activeness to use interactive media platforms. Given that the majority of advertisers and marketers are taking advantage of the interactive and participatory nature of the Web 2.0 environment (Sweetser, 2010), the added knowledge of consumers different activeness can help them develop more accurate media planning strategies, such as evaluating the user s degree of activeness among different media platforms, in conjunction with the interactive characteristics of each media platforms. For example, according to the findings from this study, it is expected that for media platforms that require more active participation from the consumers (e.g. Facebook or YouTube), consumers are more motivated to watch OVAs to meet their needs for social relaxation, information, escapism-pass time, and entertainment compared to media platforms that require less active participation (e.g. forwards or portal websites). In fact, even consumers who want to simply escape from daily works and pass time use active ways to access OVAs. Therefore, considering the relationships between motivations and access modes to watch OVAs, it is assumed that video-sharing websites or social networking sites are more effective places to expose OVAs to target consumers rather than portal sites or direct s. Future research can assess different access modes as either predictors of certain motivation factors or whether there are demographic differences among access mode behaviors in viewing OVAs. In terms of the relationship between the set of motivations for viewing behavior and the set of website types, higher motivations for social interaction, relaxation, information, escapism-free time, and entertainment associate with higher frequency of visiting video-sharing websites, social networking sites, brand websites, and portal/news websites. Furthermore, higher motivation for entertainment needs is related with more frequent viewing of OVAs in video-sharing websites, while it is less likely to relate to viewing advertisements on brand websites or social networking sites. In sum, motivationally activated consumers are willing to engage in behaviors to fulfill their needs and satisfactions. The findings imply that advertising and marketing practitioners should strategically place advertisements in order to increase effectiveness. As different motivations influence the type of website for viewing OVAs, we can assume that people search different websites according to their specific needs. Then, by placing an advertisement that stimulates the motivation associated with the characteristics of the given website, message persuasiveness may be increased as well. Interestingly, the data showed that consumers who hold entertainment motivations rarely visit brand websites or social networking sites to watch them. This finding may be associated with consumers perceived purpose of each website. Consumers may perceive that brand websites aim to persuade them to purchase their brands while social networking websites are where they interact with friends or others. It is possible that, as consumers needs lead them to experience different websites, they learn what elements of persuasion may be present in each, and this helps them cope with different websites (Friestad and Wright, 1994). Therefore, to fulfill their entertainment motivation from OVAs, they may prefer to go directly to video-sharing websites such as YouTube or Metacafe rather than other types of websites. This finding provides marketing implication in regard to how

14 Lee and Lee 1371 goal-oriented consumers behave and how advertisers and marketers can consequently place OVAs more strategically. Although the first canonical correlation analysis shows that both video-sharing websites and social networking sites are more effective platforms for OVAs than other types of websites, when considering consumers entertainment motivation, video-sharing websites are better platforms to upload OVAs. Limitations and future research As with all studies, this study also has several limitations. First, although five expected outcomes of watching OVAs were identified based on previous research, there may be other outcomes that consumers expect from watching OVAs. Qualitative research methods, such as in-depth interviews or field experiments, can provide more information about other possible expected outcomes and additional meaningful data. Additionally, the current study did not include other variables such as whether participants have perceptions about a publisher of OVAs or whether they have accounts of websites. Based on the findings of this study, future research can assess relationships between motivations and individuals perceptions or demographics related to OVAs and their websites. Finally, replicating the study with different samples can provide more explanatory and predictive power of the present research findings. Conclusion OVAs give the best of both worlds the branding power of traditional broadcast advertising and the direct and immediate response characters of the Web. This study revealed that not only do people have different motivations for viewing OVAs, but their access mode and the visited websites are also correlated with the type of motivation as well. In conclusion, advertisers should strategically place their ads on websites in conjunction with the different types of motivations consumers have for viewing OVAs. They should also understand that even within the active viewing characteristics among viewers, activeness can be segmented according to varying levels. Thus, the ability to incorporate all three elements in the designing and distributing of OVAs is fundamental. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Notes 1. College students are appropriate participants for the purpose of this study. In fact, research findings from the Pew Internet and American Life Project (2007) revealed that approximately 76% of young internet users (those between 18 and 29 years in age) watch or download online videos. Additionally, because student samples are homogeneous in nature, they are appropriate for tests of a theory-driven model (Keen, 1999). 2. Univariate outliers were detected by running frequencies in a descriptive statistical analysis and computing z-scores using the criterion of ± 3.29 S.D. above and below the mean. In this process, seven univariate outliers were identified (items that had univariate outliers), and

15 1372 new media & society 14(8) deleted from the sample. Additionally, a Mahalanobis distance of α =.001 was assessed by using linear regression to identify multivariate outliers. The Chi-square, χ 2 (50) = was determined at α =.001 from the Chi-square table, and the results indicated that there were 26 multivariate outliers in this study. 3. The first canonical correlation was.50, with approximately 25% overlapping variance. The second canonical correlation was.275, with approximately 7.6% overlapping variance. The rest of the canonical correlations were effectively zero. With all four canonical correlations included, χ 2 (20) = , p <.001, and after the first canonical correlation removed, χ 2 (12) = , p <.05. Subsequent Chi-square tests were not statistically significant. Therefore, the first two pairs of canonical variates accounted for the significant relationships between the set of motivation and the set of access modes. 4. The first canonical correlation was.498, with approximately 24.8% overlapping variance. The second canonical correlation was.242, with approximately 6% overlapping variance. The rest of two canonical correlations were effectively zero. With all four canonical correlations included, χ 2 (20) = , p <.001, and after the first canonical correlation was removed, χ 2 (12) = , p <.05. Subsequent Chi-square tests were not statistically significant. Therefore, the first two pairs of canonical variates accounted for the significant relationships between the set of motivation and the set of access modes. References Ajzen I (2002/2006) Constructing a TPB questionnaire: Conceptual and methodological considerations. Available at: (accessed 6 April 2008). Ariely D (2000) Controlling the information flow: Effects on consumers decision making and preferences. Journal of Consumer Research 27(2): Business Week (2009) Online video ads: A bag of tricks. Available at: com/technology/content/jan2009/tc _ htm (accessed 20 March 2009). Chang B-H, Lee S-E and Kim B-S (2006) Exploring factors affecting the adoption and continuance of online games among college students in South Korea. New Media and Society 8(2): Cho C-H (1999) How advertising works on the WWW: Modified elaboration likelihood model. Journal of Current Issues and Research in Advertising 21(1): Cruz D and Fill C (2008) Evaluating viral marketing: Isolating the key criteria. Marketing Intelligence & Planning 26(7): Dijkstra M, Buijtels HEJJM and Van Raaij WF (2005) Separate and joint effects of medium type on consumer responses: A comparison of television, print, and the Internet. Journal of Business Research 58(3): Dobos J (1992) Gratification models of satisfaction and choice of communication channels in organizations. Communication Research 19(1): Dobos J and Dimmick J (1988) Factor analysis and gratification constructs. Journal of Broadcasting and Electronic Media 32(3): Eckler P and Bolls P (2011) Spreading the virus: Emotional tone of viral advertising and its effect on forwarding intentions and attitudes. Journal of Interactive Advertising 11(2). Available at: (accessed 7 May 2011). emarketer (2009) Numbers show decline in TV ad spending. Available at: com/article.aspx?id= (accessed 20 March 2009). emarketer (2010) Video advertising engagement. Available at: php/emarketer-webinar-marketers-video-advertising-engagement/ (accessed 20 January 2011). Ferguson DA and Perse EM (2000) The World Wide Web as a functional alternative to television. Journal of Broadcasting and Electronic Media 44(2):

16 Lee and Lee 1373 Fishbein M and Ajzen I (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Friestad M and Wright P (1994) The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research 21(1): Grace-Farfaglia P, Dekkers A, Sundararajan B, et al. (2006) Multinational Web uses and gratifications: Measuring the social impact of online community participation across national boundaries. Electron Commerce Research 6(1): Katz E (1959) Mass communication research and the study of popular culture: An editorial note on a possible future for this journal. Studies in Public Communication 2: 1 6. Katz E, Blumler JG and Gurevitch M (1974) Utilization of mass communication by the individual. In: Blumer JG and Katz E (eds) The Uses of Mass Communications: Current Perspectives on Gratifications Research. Beverly Hills, CA: SAGE, pp Kaye B and Johnson TJ (2002) Online and in the know: Uses and gratifications of the Web for political information. Journal of Broadcasting and Electronic Media 46(1): Keen CN (1999) The attribute structure of Internet shopping: What is important and what tradeoffs are possible between internet, retail, and catalog formats? Unpublished Doctoral Dissertation, Purdue University, IN. Ko H, Cho C-H and Roberts MS (2005) Internet uses and gratifications: A structural equation model of interactive advertising. Journal of Advertising 34(2): Ko H, Roberts MS and Cho C-H (2006) Cross-cultural differences in motivations and perceived interactivity: A comparative study of American and Korean Internet users. Journal of Current Issues and Research in Advertising 28(2): Korgaonkar PK and Wolin LD (1999) A multivariate analysis of Web usage. Journal of Advertising Research 39(2): LaRose R and Eastin MS (2004) A social cognitive theory of Internet uses and gratifications: Toward a new model of media attendance. Journal of Broadcasting and Electronic Media 48(3): Lee J and Lee M (2008) Motivations of watching online video advertising: From a perspective of uses and gratifications. Paper presented at the 2008 conference of the American Academy of Advertising, San Mateo, CA, March. Lee J and Lee M (2011) Factors influencing the intention to watch online video advertising. Cyberpsychology, Behavior, and Social Networking 14(10): Leung L (2009) User-generated content on the internet: An examination of gratifications, civic engagement and psychological empowerment. New Media and Society 11(8): Levy MR and Windahl S (1984) Audience activity and gratifications: A conceptual clarification and exploration. Communication Research 11(January): Lin CA (1999) Uses and Gratifications. In: Stone G, Singletary M and Richmond VP (eds) Clarifying Communication Theories: A Hands-On Approach. Ames, IA: Iowa State University Press, pp Niekamp RA (2003) Audience activity among users of the World Wide Web. Unpublished Doctoral Dissertation, The Pennsylvania State University, PA. Norman AT and Russell CA (2006) The pass-along effect: Investigating word-of-mouth effects on online survey procedures. Journal of Computer-Mediated Communication 11(4): O Donohoe S (1994) Advertising uses and gratifications. European Journal of Marketing 28(8/9): Palmgreen P (1984) Uses and gratifications: A theoretical perspective. Communication Yearbook 8: Palmgreen P, Wenner LA and Rayburn JD (1980) Relations between gratifications sought and obtained: A study of television news. Communication Research 7(2):