Does genre type influence choice of video platform? A study of college student use of internet and television for specific video genres

Telematics and Informatics 30 (2013) 189–200

Contents lists available at SciVerse ScienceDirect

Telematics and Informatics journal homepage: www.elsevier.com/locate/tele

Does genre type influence choice of video platform? A study of college student use of internet and television for specific video genres Jiyoung Cha ⇑ Film and Video Studies, College of Visual and Performing Arts, George Mason University, 4400 University Drive MS 3E6, Fairfax, VA 22030, United States

a r t i c l e

i n f o

Article history: Received 30 June 2012 Received in revised form 16 September 2012 Accepted 21 September 2012 Available online 4 October 2012 Keywords: Online video Television Genre Internet Uses and gratifications Video platform

a b s t r a c t With the introduction of multichannel video programming distributors and different types of video platforms, consumers have more choices of channels and platforms than ever. Specifically, the present study focuses on television and the Internet as video platforms. Given the dynamics of the video programming industry, this study examines (1) how motives for watching video content predict intention to use television and intention to use the Internet as a video platform, (2) how the motives for watching a particular video content genre differ by video platform types, and (3) how audience’s choice of video content genres differs by video platform types. A survey method was used to obtain data for this study. Before the main survey was conducted, a pilot test was undertaken to test the wording of the questionnaire and reliability of items for constructs. A total of 149 students at a large university located in the southern part of the country participated in the main survey. The study reveals that motives for viewing the same genre of video content differ according to video platform types. It also discovers the underlying reason behind the popularity of certain genres online. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Video-viewing environments have changed markedly over the past few decades. Several decades ago, television was the sole video platform, and broadcast networks were the main channels for watching television programs. The addition of multichannel video programming distributors (MVPDs) to the market rapidly increased the number of television channels consumers receive. Although television is still a predominant video platform for U.S. consumers, it is worth noting that U.S. consumers increasingly use the Internet to watch video content. According to a recent ComScore report, more than 85% of U.S. Internet users watched online videos in July 2012 (ComScore, 2012). One survey conducted during 2010 found that approximately 40% of American households with broadband Internet access use the Internet to watch television programs and movies (Parks Associates, 2010). Concurrent with this trend has been the migration of advertisers from traditional mass media to online media. Top advertisers in the traditional media have decreased their allocations to this sector over the past several years. On the other hand, many advertisers have markedly invested their money online (Johnson, 2012). In particular, online video advertising is growing faster than all other online ad formats (Garcia, 2012). Online advertising spending in the U.S. was approximately $1.02 billion in 2009 and is projected to grow to nearly $7.11 billion in 2015 (Staff, 2010; Horton, 2012). Given this exponential growth of online video viewing, it is important to examine how audiences choose between television and the Internet to watch video content at any given moment. Some research has attempted to examine consumer choices of different types of media in general without examining the impact of content types. However, the consumption of a ⇑ Tel.: +1 703 993 3165; fax: +1 703 993 3175. E-mail address: [email protected] 0736-5853/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tele.2012.09.003

190

J. Cha / Telematics and Informatics 30 (2013) 189–200

medium can be better understood in relation to content along with context and consumer characteristics (Dutta-Bergman, 2004) because various aspects of content can influence the consumption of a medium (Duguid, 1996; Nunberg, 1996). In this regard, our study addresses how consumers’ use of television and the Internet differ by types of video content. A recent industry survey conducted by the Conference Board and TNS revealed that news, dramas, comedies, and reality shows are the most popular online genres, in that order (Albanesius, 2008). Even though this industry report reveals the popularity of specific video content genres online, the underlying reasons behind the popularity of those genres online are still in question. In other words, it is not clear whether the popularity of those types of video content online is based on consumers’ attraction to the genres in general across different types of video platforms. Another question that can be raised in relation to the issue includes whether consumers are more likely to watch those video genres over others because they perceive them to be more appropriate for the online platform than other genres that are less popular online. An important question on this matter may not be simply identifying the video content genres that are presently popular among consumers online. The more critical and fundamental question that can aid television networks and online video operators are the underlying reasons why particular video content genres are more accepted than other genres online. The present study aims to investigate (1) how motives for watching video content predict intention to use television and intention to use the Internet as a video platform, (2) how the motives for watching a particular video content genre differ by video platform types, and (3) how audience’s choice of video content genres differs by video platform types. Specifically, the present study focuses on television and the Internet as video platforms.

2. Literature review 2.1. Media competition and complementarities The origin of media competition theories dates back to the work of Lazarsfeld (1940), who investigated whether radio displaces print media. Over the past decades, researchers have examined how a new communication technology influences the time individuals spend using existing media. How the emergence of television (Mendelsohn, 1964), cable television (Kaplan, 1978), video (Henke and Donohue, 1989), email (Dimmick et al., 2000), or the Internet (Tsao and Sibley, 2004) influences the use of older media have been widely explored. In conjunction with the displacement effect of a new medium on older media, an essential idea of media competition theories lies in whether a new medium substitutes for, or complements, older media. Substitutability is defined as the tendency of people to switch from one product to another that fulfills the same purpose (Nicholson, 1995). The degree to which a new medium substitutes or complements an older medium depends on how consumers perceive the functional similarity between the two media and the funcational advantages of the new medium over the older medium (Althaus and Tewksbury, 2000; Levy and Windahl, 1984; Rosengren and Windahl, 1972; Williams et al., 1988). More recently, Cha and Chan-Olmsted (2012) found that functional uniqueness increases the degree that the new medium substitutes for the old medium if the old medium and the new medium are ensured to have fundamental functional similarity. For determining the degree to which a new medium substitutes for or complement to older media, it is thus essential to investigate whether a new medium achieves the same purpose sought from an existing medium. Althaus and Tewksbury (2000) suggest that media substitution is understood with the core assumption that consumers are active rather than passive. Uses and gratifications posit that audiences choose and use specific media and content to gratify their various needs (Blumler, 1979; Kim and Rubin, 1997; Levy 1987; Levy and Windahl, 1984; Perse 1990a; Rubin and Perse, 1987a,b). Based on the belief that audiences are active in making their choices (Blumler, 1979), researchers have explored how audience activity and involvement mediate outcomes. This exploration has resulted in a distinction between ritualistic and instrumental media use. Ritualistic media use tends to center on the medium per se, rather than on particular content (Rubin and Perse, 1987a). This orientation is less intentional and non-selective, and, from the viewer’s perspective, a time-filling activity in which the media is used regardless of content. Thus, it involves diversionary motives, such as habit or the passing of time (Jeffres, 1978). In contrast, instrumental orientation is more intentional and selective about content, and reflects purposive exposure to specific content (Rubin and Perse, 1987a). That is, instrumental orientation is associated more with goal-directed motivations such as information seeking, behavioral guidance, or arousal, as opposed to ritualistic orientation (Hearn, 1989; Kim and Rubin, 1997; Perse, 1990a,b; Rubin, 1983, 1984; Rubin and Perse, 1987a,b). In general, instrumental media use has been linked to increased audience activity and media involvement (Perse, 1990b). Media use orientation differs by media type. Focusing on the relationship between media and media use orientation, Metzger and Flanagin (2002) found that television use in general is more motivated by a ritualistic orientation than by an instrumental orientation. Other studies have found that media use orientation depends on the degree of consumers’ involvement with the media, along with the media’s characteristics. Perse (1990a, 1998) revealed that cable subscribers with high instrumental orientation are more likely to select and involve themselves with content using remote control devices. It is apparent that new communication technologies offer video audiences more choice of, participation with, and control over content, and thus more opportunity for activity (Metzger and Flanagin, 2002). With respect to Internet use in general, Papacharissi and Rubin (2000) found that the Internet is more oriented toward instrumental use than ritualistic use. Even though the primary motives for Internet use can be fivefold, including information seeking, interpersonal utility, passing time, convenience, and entertainment, Papacharissi and Rubin suggested that the most salient motive for Internet use is

J. Cha / Telematics and Informatics 30 (2013) 189–200

191

information seeking, which reflects an instrumental orientation. Concurrent with Papacharissi and Rubin (2000); Ferguson and Perse (2000) pointed out that the Web is not as efficient as television for rest and relaxation because a certain level of involvement and stimulation is necessary for Web use. In addition, Ferguson and Perse (2000) revealed that entertainment, too, is a salient motive behind Web use and the Web can gratify audiences’ ritualistic diversion as well. While earlier studies found that Internet use is skewed to instrumental orientation, a later study suggests a somewhat contradictory finding. In a direct comparison between ritualistic and instrumental orientations, Metzger and Flanagin (2002) found no significant difference between the former and the latter for Internet use. Since the Internet was introduced to the general public, Internet technologies have advanced considerably. Broadband has been the dominant bandwidth people have been using in U.S. households since 2005 (Arbitron, 2006), and its increasing use has led to the marked diversification of online content and services. As a result, it appears that the Internet has become a more balanced medium that gratifies both ritualistic and instrumental motives; however, in its early stage, it became a medium that was more likely to gratify instrumental motives. In the context of video viewing, the Internet provides audiences with more control of content and schedule than television does. Online video viewing requires more involvement and activity than television viewing. Some audiences are aggressively employing the Internet as a means to watch video content. Given the aforementioned dynamics involving video content consumption, this study raises a question – whether and how television and the Internet differ as video platforms. To that end, this study addresses whether and how the motives behind video content consumption differ between television and the Internet as video platforms. RQ1: What motives for watching video content are related to intention to use television? What motives for watching video content are related to intention to use the Internet as a video platform? Are there motivational differences for using television and the Internet to watch video content? 2.2. Genres and video platform choices Uses and gratifications theory suggests that people choose a medium to satisfy their specific needs. In light of uses and gratifications, different motives for watching video content should predict the use of different video platforms. Indeed, prior studies have revealed that how much learning is sought from news differs, depending on the medium people use (Chaffee and Choe, 1981; Chaffee and Schleuder, 1986; Prior, 2005). Vicent and Basil (1997) found that newspaper reading is a predictor of current events knowledge, whereas watching television news is not. Diddi and LaRose (2006) also found that entertainment needs correlated with print news, but entertainment needs did not correlate with television news. Based on the uses and gratifications theory, the present study proposes the following hypothesis and addresses the research question below: H1. Motives for watching a particular genre of video content differ between television and the Internet. RQ2. How do video consumption motives predict intention to use television to watch a particular genre of video content? How do video consumption motives predict intention to use the Internet to watch a particular genre of video content? How are the motivational predictors of the consumption of a video genre different according to whether individuals choose television or the Internet? Uses and gratification theory suggests that consumers choose and use a medium or content to fulfill their specific needs. Hence, the genre of video content chosen by consumers using a particular type of video platform may depend on the fit between the video platform and the content in satisfying specific needs of consumers. No matter whether consumers choose a video platform type first or a video genre first, the type of genre using a specific type of video platform might depend on the perceived fit between the video genre and the video platform that satisfies consumers’ needs. People might prefer to watch news instead of a series of dramas or comedies through the Internet because of their perception or belief that the Internet is more appropriate for searching for specific information and updates for current events. If individuals perceive television to be better for relaxation than the Internet as a video platform, some might prefer watching sitcoms and comedies using television instead of the Internet. Focusing on the preference of content genres in the online video viewing context, a recent industry report undertaken by the Conference Board and TNS showed that news is the most popular video content online, attracting about 43% of respondents. Drama follows closely behind with 39%, while sitcoms and comedy programs enjoy a 34% following. Reality shows garnered 23% of the audience, while sports attracted 16% of respondents (Albanesius, 2008). Although a few industry reports have indicated which video genres are popular online, there is scarce research that examined why those video genres are popular online. Are certain genres particularly popular online because consumers believe that online viewing is more suitable for those video genres? If that is correct, there might be a discrepancy between popular genres on television and online. A group of theories counters the argument. These theories contend that popular video genres are the same across different types of video platforms, because consumers’ preference of video genres influences what genre of video content they will watch, no matter what type of video platforms they use. Theories of program choice postulate that audiences have preferences for specific program characteristics (Bowman 1975; Lehmann 1971) or program types (Youn 1994). Selective exposure theory postulates that individuals orient their attention to specific stimuli in an environment (Zillman and Bryant, 1985). That is, the selection is derived from individuals’ interests in a specific subject area (Petty and Cacioppo, 1986). Specifically focusing on cable television and the Internet, Prior (2005) suggested that audiences choose the content type that best satisfies their preferences. Wober and Gunter (1986) pointed out that people tend to stick to favorite genres although their preferences for specific shows and particular episodes may vary.

192

J. Cha / Telematics and Informatics 30 (2013) 189–200

Prior (2005) suggested that when audiences have access to different types of media, content preference strongly predicts the selection of the content and the preference is reinforced. It was found that people who have a relative entertainment preference are less likely to learn news when they have access to both cable television and the Internet. On the other hand, people who have a relative entertainment preference are more likely to learn news than people who have no access to either cable or the Internet. That is, more media options allow individuals to reinforce their preferences of particular content types, whereas constraints to platforms cause people to choose content types they may not prefer. The author of the study suggested that people who prefer a specific genre of video content tend to watch the video content genre over other video content genres across various media platforms (Prior, 2005). Apart from the difference in content consumption between consumers of online and traditional media, Dutta-Bergman, 2004) also found that individuals who read a specific domain of news online are more likely to consume the domain of news on traditional media, including television, radio, and newspapers than those who do not read the domain of news online. Specifically, they focused on the domains of politics, sports, business and finance, science and health, international affairs, local government, and entertainment news. The results were consistent across the domains of news. Baum and Kernell (1999) revealed that the diverse accessibility of television channels caused by platform options reinforce consumers’ preferences for certain types of content. Comparing broadcast television only audiences and cable television subscribers, Baum and Kernell (1999) found that cable television subscribers are less likely to watch presidential debates than individuals who receive broadcast television only. They found that the advent of cable television increased the number of program options for audiences and, thus, cable television caused the knowledge gap between people with a preference for news and people with a preference for other types of content. From the managerial perspectives of both television firms and online video service providers, it is important to know whether the type of video platform influences audiences’ choices of specific genres of video content and whether audiences’ preferences of particular genres of video content carries over across different types of video platforms. If the audience’s choice of video content genre differs across video platform types, the addition of an online distribution platform would complement an offline distribution platform for television firms. For instance, suppose that some people barely watch news on television, but they may watch news videos online because the Internet allows them to search for and locate the news video they are most interested in easily. Similarly, some people who prefer to watch comedies and dramas on television may prefer education or how-to videos when they use the Internet as a video platform. To address the question, this study investigates the discrepancy of audience consumption of a particular video content genre between television and the Internet. Given that television is still a prevalent medium compared to the online platform for watching video content, some genres of video content are more readily available on television than online. In addition, how much access people have to television and the Internet may influence their video genre choice. The accessibility of television and the Internet may depend on many different variables, such as living situation, work, etc. Thus, this study focuses on the likelihood of people watching a particular genre of video content on television and online instead of examining the actual consumption of the video content genre. RQ3. What genres of video content do consumers intend to consume more by using television rather than using the Internet (vice versa)? Are there differences between television and online with respect to video content genres that are most and least likely to be consumed?

3. Method 3.1. Measures To measure motives for consumption of video content, this study adapted items from Rubin (1981, 1984). The measures adapted from Rubin (1981, 1984) were successful in examining whether the Internet serves as a functional alternative to television (Ferguson and Perse, 2000). Some of the Internet-related motive items were also adapted from Flavian and Gurrea (2007). The motives behind Internet use were integrated to capture the discrepancy between television and the Internet as different forms of video platforms. The respondents were asked to indicate their agreement with each of the statements that describe motives for video content consumption on a seven-point Likert Scale (1 = strongly disagree, 7 = strongly agree). To assess intention to watch a specific type of video content using television and the Internet, respondents were asked to indicate on a seven-point scale (1 = very unlikely, 7 = very likely) how likely they are to watch each of the video content genres using television and the Internet. The measures were adapted from Phau and Poon (2000). With respect to video content genres, the present study focused on eight types of video content, including comedies, dramas, reality shows, news, previews/recaps, documentaries, entertainment magazines, and education/how to programs. The genre categories were based on Hall (2005); Potts et al. (1996), and Preston and Clair (1994). To measure intention to use television and the Internet for viewing video content in general without asking about the use by content types, respondents were asked to indicate their likelihood of using television and the Internet to watch video content on a seven-point scale (1 = very unlikely, 7 = very likely). Even though this study covers a variety of video content genres, the list does not cover all of them. Thus, instead of combining the likelihood to use television or the Internet to watch each of the eight video content genres, the present study used one single item to measure consumers’ intention to use television or the Internet to watch video content.

J. Cha / Telematics and Informatics 30 (2013) 189–200

193

To identify users and non-users of an online platform for each video content genre, actual consumption of a specific video content genre using the Internet was measured on a dichotomous scale (1 = yes, 2 = no) as Dutta-Bergman (2004) did. 3.2. Sample and procedures A survey method was used to obtain data for this study. Before the main survey was conducted, a pilot test was undertaken to test the wording of the questionnaire and reliability of items for constructs. Based on the results of the pilot test, the wording and order of the questions were slightly modified. College students were selected as a sample for this study. College students comprise one audience subgroup defined by age and role (Pingree et al., 2001). College students are not necessarily representative of a general population. A SurveyU report revealed that the number of U.S. college students who use online video markedly exceeds that of general U.S. adult Internet users. According to the report, only 57% of U.S. adult Internet users had ever watched online video as of 2007, whereas 93% of U.S. college students had watched online video. The same study found that general Internet users mostly watch news on the online video platforms, whereas college students watch comedy most online (SurveyU, 2007). A Nielsen survey also found that people ages 18–24 spend more time watching videos on the Internet than do older age groups, but less time watching videos on television (NB, 2008). College students are early adopters and heavy users of the Internet compared to the general population (Jones et al., 2009; Nie et al., 2005). Today’s college students are also the first generation of Internet users; they grew up with and have always known the medium and video technologies. The aformentioned characteristics of college students as early adopters and heavier users of the Internet, video technologies, and online videos help researchers predict the plausible dynamic interplay between television and online video platforms in five or ten years. The homogeneous characteristics of college students also prompt many media to specifically target college students (Barnhurst & Wartella, 1998). As college students increasingly spend more time on the Internet than watching television, television firms may fear that this desirable target group will end up continuing to reduce the amount of time they spend watching television. In order to have a better understanding of this important demographic group for television firms, it is also pivotal to examine underlying reasons behind the choice of a video platform to watch certain types of video content among college students. A total of 149 students at a large university located in the southern part of the country participated in the main survey. The participants were recruited from a few large lecture classes that were open to various majors. The mean age of the participants was 20.18 (SD = 2.26). The sample breakdown was 32.2% male (n = 48) and 67.8% (n = 101) female; 10.7% of the participants were freshmen (n = 16), 28.9% sophomores (n = 43), 29.5% juniors (n = 44), and 30.9% seniors (n = 46). Caucasians accounted for 63.1% of the survey participants; African Americans made up 14.8%, Hispanics made up 12.8%; and Asians accounted for 6.0%. The results of the repeated measures ANOVA indicate that there exist statistically significant differences between the frequency of using television and the frequency of using the Internet for watching video content. The ANOVA results show that television (M = 5.77, SD = 1.28) is used more often than the Internet (M = 4.77, SD = 1.71) for watching video content (F = 37.13, p < .001). With respect to the primary video platform, 77.2% of the respondents chose television as the primary video platform, whereas 22.8% of the respondents said they used the Internet as their primary video platform. There are two types of online venues where people watch video content. One is video sharing Web sites, such as YouTube and DailyMotion. The other type of venue is Web sites affiliated to television networks, such as ABC.com and Hulu.com. The college students more frequently used video sharing sites (M = 4.26, SD = 1.74) than Web sites affiliated with television networks (M = 4.06, SD = 1.53). Even though the college students use video sharing sites more often than Web sites affiliated with television networks, this survey interestingly reveals that they watch branded videos that are originally produced by media firms (M = 4.27, SD = 1.46) more often than user-generated videos (M = 4.05, SD = 1.46). This data is consistent with the industry reports showing that users of online video sites actually prefer content from major media brands over user-generated content (Holahan, November 20, 2007). The favorite video content genres among college students are comedy (M = 6.76, SD = .50), dramas (M = 5.82, SD = 1.13), and news (M = 5.05, SD = 1.27). Previews/recaps (M = 4.37, SD = 1.43) and education/how to programs (M = 4.41, SD = 1.32) were their least favorites. 3.3. Statistical analysis To investigate the motivations for watching video content, an exploratory factor analysis using a varimax rotation was first carried out. With the resultant factors, multiple regression analyses were undertaken to identify the motives that are related to intention to use television and the Internet for watching video content, respectively. To explore whether consumers were more likely to intend to use the Internet or television for a particular video content genre, one-way ANOVA with repeated measures was performed. 4. Results RQ1 addressed what motives for watching video content are related to intention to use television and the Internet. The subsequent question is: Are there motivational differences between using television and the Internet to watch video

194

J. Cha / Telematics and Informatics 30 (2013) 189–200

content? The factor analysis with varimax rotation resulted in the deletion of four motivation items due to the several high loadings across the factors (Hair et al., 1995). As presented in Table 1, the factor analysis extracted a total of eight factors, explaining 71.21% of the variance. The first motivation factor represents getting updates on current events. The second motivation factor is relief of boredom. The third factor contains items that reflect relaxation motive. The fourth factor explains entertainment motive. The fifth factor represents companionship. The sixth factor reflects escape motive, and the seventh factor contains items that represent habit. The eighth motivation factor is the motive for social interaction. Each of the motive variables was reliable. The Cronbach’s alphas are above .70. After factor analysis yielded eight factors, the regression analyses using the resultant factors were carried out. The result indicates that the motives affecting intention to use television and the Internet as a video platform are quite different. Four motivations for video content consumption were statistically significantly related to consumer’s intention to use television (see Table 2). Entertainment has the strongest relationship with college students’ intention to use television (b = .32, p < .001). Along with entertainment, boredom relief (b = .21, p < .05), social interaction (b = .18, p < .05), and habit (b = .18, p < .05) have statistically significant relationships with the likelihood to use television. Interestingly, boredom relief and social interaction motives were negatively related to the intention to use television. The analyses revealed that different motives are related to intention to use the Internet for watching video content. Relaxation (b = .22, p < .05) and getting updates on current events (b = .17, p < .05) were significantly related to intention to use the Internet for watching video content. H1 proposed that motives for watching a particular genre of video content differ between television and the Internet. RQ2 asked how different are the video consumption motives that predict intention to use television and intention to use the Internet to watch the same genre of video content. An array of multiple regressions was performed to investigate whether and how the motives for the consumption of a particular video content genre differ across video platform types. The results of the regression analyses are presented in Table 3a and b. The results indicate that the motives for watching a specific type of video content are different according to whether individuals choose television or the Internet. Therefore, the hypothesis 1 was supported. Overall, habit has a positive relationship with television use across several genres of video content – comedies (b = .24, p < .01), reality shows (b = .22, p < .01), previews/recaps (b = .31, p < .001), entertainment magazines (b = .23, p < .01),

Table 1 Factor analysis of motives for watching video content. Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Factor 7

Factor 8

.86 .79 .74 .69

.02 .16 .08 .06

.13 .01 .02 .02

.05 .25 .19 .01

.01 .08 .02 .19

.01 .16 .20 .08

.17 .06 .01 .12

.01 .06 .08 .25

.67 .65 .63

.03 .15 .13

.06 .05 .07

.02 .18 .20

.04 .16 .26

.33 .31 .10

.07 .01 .11

.05 .04 .07

Factor 2: Boredom relief Because it passes time when I am bored When I have nothing better to do Because it gives me something to do to occupy my time

.07 .11 .02

.90 .83 .75

.01 .14 .05

.10 .15 .05

.08 .01 .08

.11 .04 .32

.11 .12 .09

.01 .15 .00

Factor 3: Relaxation Because it allows me to unwind Because it relaxes me Because it’s pleasant rest

.03 .02 .03

.12 .10 .00

.88 .86 .67

.10 .03 .22

.11 .21 .17

.05 .06 .43

.03 .00 .10

.04 .03 .01

Factor 4: Entertainment Because it entertains me Because it’s enjoyable

.05 .00

.11 .08

.06 .21

.88 .86

.12 .05

.03 .08

.03 .07

.09 .13

Factor 5: Companionship So I won’t have to be alone Because it makes me feel less lonely When there’s no one else to talk to or be with

.09 .12 .16

.11 .12 .34

.09 .05 .34

.03 .17 .09

.86 .85 .64

.21 .09 .12

.04 .13 .13

.02 .01 .08

Factor 6: Escape So I can get away from what I’m doing So I can get away from the rest of the family or others

.05 .12

.30 .19

.17 .04

.21 .08

.06 .30

.72 .58

.03 .27

.01 .11

Factor 7: Habit Because it’s a habit, just something I do Just because it’s there

.07 .08

.14 .13

.07 .04

.05 .49

.14 .07

.12 .18

.80 .62

.12 .23

.08 .08 5.76

.02 .16 3.95

.03 .01 2.68

.14 .08 2.26

.09 .04 1.64

.15 .19 1.40

.04 .24 1.18

.87 .73 1.08

Factor 1: Getting updates on current events To find constantly updated event information Because I am interested in current events To find breaking news events Because I am interested in the immediacy with which information can be obtained So I could learn about what could happen to me Because it helps me learn things about myself and others So I can learn how to do things which I haven’t done before

Factor 8: Social interaction So I can be with other members of the family or friends who are watching Because it’s something to do when friends come over Eigen value

195

J. Cha / Telematics and Informatics 30 (2013) 189–200 Table 2 Predictors of intention to use television and the internet. Motivations

Television

Internet t

b Getting updates on current events Boredom relief Relaxation Entertainment Companionship Escape Habit Social interaction R2 Adjusted R2 * ** ***

.01 .21* .06 .32*** .11 .16 .18* .18* .17 .13

t

b .17* .00 .23** .07 .14 .13 .07 .02 .08 .03

.14 2.31 .73 3.72 1.20 1.75 2.05 2.17

1.96 .04 2.44 .81 1.50 1.36 .76 .26

p < .05. p < .01. p < .001 (two tailed).

Table 3 Motives for watching a specific genre of video content using television and the internet. Comedy

(a) Getting updates on current events Boredom relief Relaxation Entertainment Companionship Escape Habit Social interaction R2 Adjusted R2

Drama Internet b

Television b

Internet b

.08 .11 .16 .34*** .06 .01 .24** .05 .21 .17

.06 .07 .19* .14 .04 .17 .06 .10 .10 .05

.02 .08 .29*** .27** .02 .01 .11 .08 .20 .15

.07 .03 .21* .06 .02 .09 .04 .02 .06 .00

Preview/recap

(b) Getting updates on current events Boredom relief Relaxation Entertainment Companionship Escape Habit Social interaction R2 Adjusted R2 * ** ***

Reality Show

Television b

Documentary

Television b .06 .07 .03 .16 .06 .02 .22** .03 .10 .05

News Internet b

Television b

Internet b

.03 .18 .04 .21* .12 .01 .01 .03 .10 .05

.56*** .08 .12 .12 .24** .04 .00 .15* .31 .27

.54*** .29*** .03 .04 .19* .07 .05 .09 .31 .27

Entertainment magazine

Television b

Internet b

Television b

Internet b

.11 .04 .12 .14 .00 .04 .31*** .10 .14 .09

.19* .00 .11 .13 .03 .10 .22* .07 .11 .05

.36*** .08 .04 .02 .08 .04 .08 .19* .15 .10

.42*** .06 .11 .03 .06 .16 .05 .14 .20 .16

Television b .11 .11 .05 .08 .01 .02 .23** .07 .15 .10

Education/how to Internet b

Television b

Internet b

.16 .22* .34 .09 .09 .08 .02 .10 .11 .06

.44*** .09 .09 .11 .04 .10 .20* .13 .25 .20

.34*** .07 .05 .02 .08 .10 .07 .02 .13 .08

p < .05. p < .01. p < .001 (two tailed).

and education/how-to programs (b = .20, p < .05). Social interaction has a negative relationship with intention to use television to watch news (b = .15, p < .05) and documentaries (b = .19, p < .05). Boredom relief is not related to intention to use television to watch news and entertainment magazines. However, boredom relief is positively related to the intention to use the Internet to watch news (b = .29, p < .001) and entertainment magazines (b = .22, p < .05). Getting updates on current events is commonly and positively related to intention to use television or the Internet to watch news, documentaries, and education/how-to programs.

196

J. Cha / Telematics and Informatics 30 (2013) 189–200

RQ3 addressed what genres of video content consumers intend to consume more by using television rather than using the Internet (vice versa); it also asked whether there exist differences with respect to video content genres that are most and least likely to be consumed using either television or online. To answer the first part of the research question, repeated measures of ANOVA were conducted. Table 4 presents the results repeated measures of ANOVA. The results indicate that people are more likely to use television than the Internet to watch all of the video content genres this study examined – comedies, dramas, reality shows, preview/recaps, documentaries, entertainment magazines, and education/how- to programs. There were no video genres that consumers intend to consume more by using the Internet rather than television. To find video genres that are the most and least likely to be consumed using television and the Internet, a one-way repeated measures ANOVA was performed for both platforms. Bonferroni’s simultaneous 95% confidence intervals were used to jointly compare the mean scores of the likelihood of watching video genres using television. As seen in Table 5, there are significant mean differences between the likelihood of watching video genres using television (F = 68.22, p < .001). Comedies (M = 6.12, SD = 1.12) are most likely to be consumed using television, followed by dramas (M = 5.50, SD = 1.46), news (M = 5.10, SD = 1.65) and reality shows (M = 4.45, SD = 1.92). Previews (M = 3.66, SD = 1.76), documentaries (M = 3.50, SD = 1.75), and education/how-to programs (M = 3.27, SD = 1.71) are least likely to be consumed using television. With respect to online video platforms, there are also significant mean differences between the likelihood of watching genres (F = 36.78, p < .001). Comedies (M = 4.83, SD = 1.73) are most likely to be watched, followed by dramas (M = 4.22, SD = 1.94), news (M = 4.13, SD = 2.02), reality shows (M = 3.08, SD = 1.96), and previews (M = 3.28, SD = 1.98). Documentaries (M = 2.41, SD = 1.58) are least likely to be watched using online video platforms (see Table 5). The ANOVA results from both television and online video platforms indicated that the types of video genres that are most or least likely to be watched were similar across television and online video platforms. 5. Discussion and conclusions Since the emergence of the Internet, empirical studies have examined whether motives behind the use of the Internet differ from motives to watch television, whether the Internet can be an alternative to television, and whether the Internet displaces television (e.g., Ferguson and Perse, 2000; Lee and Kuo, 2002; Arbitron, 2006). Most of these studies examined the interplay between the Internet and television in general, without delving into the role of the Internet as a video platform. Given the scarce research, the present study attempts to examine how similar or different the Internet and television are with respect to consumers’ motives to watch video content. Further, this study investigates whether television and the Internet function differently, although consumers watch the same genre using each type of video platform into fulfill their needs. This study’s findings highlight that consumer’s motives for video content consumption as well as for viewing the same genre of video content are different according to the modality of video platforms (i.e., television and the Internet). The findings from this study suggest that the Internet as a video platform is not a perfect substitute for television, but it can grow as a threat to television. Specifically, this study found that entertainment, boredom relief, habit, and social interaction motives influence intention to use television. On the other hand, relaxation and getting updates on current events increase the likelihood of using the Internet as a video platform. Different consumer needs predict the intention to use each type of video platform. Specifically, entertainment and habit motives boost the likelihood of using television. The finding that entertain-

Table 4 Intention to use television and the internet according to genres of video content. Video content type

Platforms

Intention to use each of the platforms M (SD)

F

Comedy

Television Internet Television Internet Television Internet Television Internet Television Internet Television Internet Television Internet Television Internet

6.10 4.85 5.51 4.22 4.43 3.07 5.09 4.16 3.63 3.27 3.52 2.46 3.68 2.80 3.24 2.78

63.52***

Drama Reality show News Preview/recap Documentary Entertainment magazine Education/how to

* ** ***

p < .05. p < .01. p < .001.

(1.13) (1.72) (1.47) (1.93) (1.94) (1.95) (1.64) (2.00) (1.76) (1.98) (1.76) (1.61) (2.07) (1.89) (1.70) (1.83)

63.42*** 72.36*** 47.51*** 6.07* 53.72*** 28.97*** 9.37**

197

J. Cha / Telematics and Informatics 30 (2013) 189–200 Table 5 Repeated measures ANOVA Results for video content genres likely to be consumed on each video platform. Intention to use television to watch the genre of video content Mean difference Comedy –News Comedy – Drama Comedy – Reality show Comedy – Entertainment mag. Comedy – Preview/recap Comedy – Documentary Comedy –Education/how to News – Comedy News – Drama News- Reality show News – Entertainment mag. News – Preview/recap News – Documentary News – Education/how to Drama – Comedy Drama – News Drama – Reality show Drama - Entertainment mag. Drama – Preview Drama – Documentary Drama – Education/how to. Reality show – Comedy Reality show – News Reality show – Drama Reality show – Entertainment mag. Reality show – Preview Reality show – Documentary Reality show – Education/how to Entertainment mag.– Comedy Entertainment mag.– News Entertainment mag.– Drama Entertainment mag.– Reality show Entertainment mag.– Preview Entertainment mag. – Documentary Entertainment mag. – Education/how to Preview – Comedy Preview – News Preview – Drama Preview – Reality show Preview – Entertainment mag. Preview – Documentary Preview – Education/ how to Documentary – Comedy Documentary – News Documentary – Drama Documentary – Reality show Documentary – Entertainment mag. Documentary – Preview Documentary – Education/how to Education/how to – Comedy Education/how to – News Education/how to – Drama Education/how to – Reality show Education/how to – Entertainment mag. Education/how to – Preview Education/ how to – Documentary

Mean difference 1.02⁄ .62⁄ 1.67⁄ 2.41 2.46⁄ 2.62⁄ 2.85⁄ 1.02⁄ .406 .650⁄ 1.39⁄ 1.44⁄ 1.60⁄ 1.83⁄ .62⁄ .41 1.06⁄ 1.79⁄ 1.85⁄ 2.01⁄ 2.23⁄ 1.67⁄ .65⁄ 1.06⁄ .73⁄ .79⁄ .95⁄ 1.18⁄ 2.41⁄ 1.36⁄ 1.79⁄ .73⁄ .06 .22 .44 2.46⁄ .144⁄ 1.85⁄ .79⁄ .06 .16 .39 2.62⁄ 1.66⁄ 2.01⁄ .95⁄ .22 .16 .22 2.85⁄ 1.83⁄ 2.23⁄ 1.18⁄ .44 .39 .22

Intention to use online video platforms to watch the genre of video content

Std error .14 .10 .16 .18 .14 .16 .16 .14 .16 .19 .20 .17 .15 .15 .10 .16 .18 .19 .15 .16 .16 .16 .19 .18 .17 .19 .19 .20 .18 .20 .19 .17 .19 .20 .18 .14 .17 .15 .19 .19 .15 .16 .16 .15 .16 .19 .20 .15 .13 .16 .15 .16 .20 .18 .16 .13

Mean difference Comedy –News Comedy – Drama Comedy – Reality show Comedy – Entertainment mag. Comedy – Preview/recap Comedy – Documentary Comedy –Education/how to News – Comedy News – Drama News- Reality show News – Entertainment mag. News – Preview/recap News – Documentary News – Education/how to Drama – Comedy Drama – News Drama – Reality show Drama – Entertainment mag. Drama – Preview Drama – Documentary Drama – Education/how to Reality show – Comedy Reality show – News Reality show – Drama Reality show – Entertainment mag. Reality show – Preview Reality show – Documentary Reality show – Education/how to Entertainment mag. – Comedy Entertainment mag. – News Entertainment mag. – Drama Entertainment mag. – Reality show Entertainment mag. – Preview Entertainment mag. – Documentary Entertainment mag. – Education Preview – Comedy Preview – News Preview – Drama Preview – Reality show Preview – Entertainment mag. Preview – Documentary Preview – Education/how to Documentary – Comedy Documentary – News Documentary – Drama Documentary – Reality show Documentary – Entertainment mag. Documentary – Preview Documentary – Education/how to Education/how to – Comedy Education/how to – News Education/how to – Drama Education/how to – Reality show Education/how to – Entertainment mag. Education/how to – Preview Education/how to – Documentary

.70⁄ .62⁄ 1.75⁄ 2.05⁄ 1.56⁄ 2.42⁄ 2.13⁄ .70⁄ .08 1.05⁄ 1.35⁄ .85⁄ 1.72⁄ 1.43⁄ .62⁄ .08 1.13⁄ 1.43⁄ 9.37⁄ 1.80⁄ 1.51⁄ 1.75⁄ 1.05⁄ 1.13⁄ .30 .20 .67⁄ .38 2.05⁄ 1.35⁄ 1.43⁄ .30 .50 .37 .08 1.55⁄ .85⁄ .94⁄ .20 .50 .87⁄ .57 .24⁄ 1.72⁄ 1.80⁄ .67⁄ .37 .87⁄ .29 2.13⁄ 1.43⁄ 1.15⁄ .38 .08 .57 .29

Std error .19 .15 .21 .19 .18 .18 .19 .19 .21 .23 .18 .20 .17 .16 .15 .21 .20 .19 .19 .19 .20 .21 .23 .20 .18 .21 .20 .22 .19 .18 .19 .18 .19 .18 .20 .18 .20 .19 .21 .19 .16 .18 .18 .17 .19 .20 .18 .16 .13 .19 .16 .20 .22 .20 .18 .13

p < .05.

ment motive has the strongest impact on the intention to use television corroborates the findings of previous studies (Rubin, 1981, 1983, 1984). Ferguson and Perse (2000) found that college students use the Web most for entertainment, and thus suggested that the Web may replace television if the Web provides more entertainment. The present study shows that there is still a gap between television and the Internet as a video platform with respect to the extent to which each video platform fulfills entertainment needs.

198

J. Cha / Telematics and Informatics 30 (2013) 189–200

Also, this study paid acute attention to the role of habit in the intention to use television. Habit has a relationship not only with television use in general, but also with intention to use television to watch several genres of video content – comedies, reality shows, previews/recaps, entertainment magazines, and education/how-to programs. The media attendance theory could explain the influence of habit on intention to use television (LaRose and Eastin, 2004). The media attendance theory proposes that active selection processes of media consumption, assumed by uses and gratifications primarily operate in the early stages of media selection. After consumers pass the early stage, their habitual patterns dominate their media consumption patterns in order to conserve mental resources; they are less likely to repeatedly engage in active selection (Diddi and LaRose, 2006; LaRose, 2010). Overall, consumers became considerably familiar with television as a predominant video medium over the past several decades. Their repeated use of television over the past years made consumers habitually use television and habitually watch certain genres of video content on television – even though they initially started using television and watched video content with a goal-oriented instrumental orientation. In contrast, consumers apparently have not yet developed routines and habits regarding the Internet as a video platform. Another interesting finding from this study is that boredom relief is actually negatively related with television use. Nearly 30 years ago, when consumers did not have many platform choices other than television, boredom relief was the third strongest motive that increased television use (Rubin, 1981, 1983, 1984). The result from this study contradicts the findings from the previous studies. A possible explanation may be that the emergence of alternative video platforms, such as online and mobile video platforms, lessens the boredom relief by television use. Given that television has a relatively limited mobility compared to online video platforms and mobile video platforms, college students seem to use other types of video platforms to fulfill their boredom relief needs. Although the current study selected college students as a subgroup of a general population, the characteristics of college students as early adopters and heavy users of Internet-based systems may point to a future trend for television use. Therefore, this result may imply that boredom relief would not be as important as before in predicting television use. Future studies can expand the sample to other demographic groups to examine further how boredom relief is related to television and online video platform use. Note that the current study, surprisingly, shows that the relaxation motive behind video content consumption is the most important predictor for individuals’ intention to use the Internet for watching video content. Getting updates on current events is the second strongest predictor of intention to use the Internet to watch video content. This study also revealed that relaxation is not related with intention to use television. Most prior studies have neglected to include both relaxation needs and seeking information needs concurrently in their models in examining Internet use (e.g., Papacharissi and Rubin, 2000; Diddi and LaRose, 2006; Ferguson and Perse, 2000). Thus, a contribution of the current study is that it shows how a relaxation motive and seeking information motive relatively influence the use of different types of video platforms. It is apparent that young audiences seem increasingly to feel online video platforms to be better places to go to relax than television. Today’s college students grew up with computers and they are accustomed to being connected to them 24/7. Thus, their needs for relaxation are more tied to the use of online video platforms rather than television use. Ferguson and Perse (2000) found that relaxation is significantly linked to play and affinity activities on the Web. Hence, it appears that relaxation needs outweigh the desire to get updates on current events when predicting the use of the Internet as a platform to watch video content. Considering that relaxation was the second strongest predictor of television use in studies conducted prior to the multiplatform era (Rubin, 1981, 1983, 1984), online video platforms may grow as a threat to usage of television among college students. The use of online video platforms for relaxation would also grow in the future as younger people increasingly use online video platforms as time progresses. It is also noteworthy that the social interaction motive for watching video content has a negative relationship with the intention to use television in general. The result indicates that the needs for seeking social interaction through co-viewing reduce the likelihood of using television among college students. Historically, television has been considered a social medium because families and friends gathered to watch television together. On the other hand, the result of this study implies that college students who seek more social interaction from video consumption use other types of media rather than television. The study also further reveals that college students are less likely to use television to watch news and documentaries if they seek to interact with their family and friends through co-viewing. With respect to popular genres on each video platform, the general preference for a video genre without distinction between video platforms determines what genre of video content consumers will watch, no matter whether they use television or the Internet. College students are most likely to watch comedies, dramas, and news, regardless of whether they choose the Internet or television. College students are least likely to watch documentaries and education/how-to programs, regardless of the video platform type. The different characteristics of television and the Internet as video platforms do not play a significant role in determining what genres consumers watch using a particular video platform. Therefore, this study concludes that some genres of video content are more popular than other genres online – because they are universally more popular across different video platforms. Future studies can further explore this topic from the standpoint of the displacement effect. In addition, other studies can investigate whether the length of video content has any impact on consumers’ choice of the Internet or television. This study focused on college students because, along with teenagers, they are one of the most avid users of online videos (Quantcast, 2012). Nevertheless, it should be noted that the data were obtained from a single university, even though there have been efforts to collect data across diverse majors on campus. Whether the internet as a video platform substitutes for television may differ across different demographic groups. Other studies can examine this question by using a larger sample of people in various age groups.

J. Cha / Telematics and Informatics 30 (2013) 189–200

199

References Albanesius, C., 2008. Who needs a TV? Web video viewing doubles. PC Magazine. (retrieved 10.09.11). Althaus, S., Tewksbury, D., 2000. Patterns of Internet and traditional news media use in a networked community. Political Communication 17 (1), 21–45. Arbitron, 2006. Internet and multimedia 2006. (retrieved 10.12.11). Barnhurst, K.G., Waterlla, E., 1998. Young citizens, American TV newscasts and collective memory. Critical Studies in Mass Communication 15 (3), 279–305. Baum, M.A., Kernell, S., 1999. Has cable ended the golden age of presidential television? American Political Science Review 93 (1), 99–114. Blumler, J., 1979. The role of theory in uses and gratifications studies. Communication Research 6 (1), 9–36. Bowman, G., 1975. Consumer choice and television. Applied Economics 7 (3), 175–184. Cha, J., Chan-Olmsted, S., 2012. Substitutability between online video platforms and television. Journalism and Mass Communication Quarterly 89 (2), 261– 278. Chaffee, S.H., Choe, S.Y., 1981. Newspaper reading in longitudinal perspective: beyond structural constraints. Journalism Quarterly 58 (2), 201–211. Chaffee, S.H., Schleuder, J., 1986. Measurement and effects of attention to news media. Humman Communication Research 13 (1), 76–107. ComScore, 2012. ComScore releases July 2012 U.S. online video rankings. (retrieved 08.09.12). Diddi, A., LaRose, R., 2006. Getting hooked on news: uses and gratificiatons and the formation of news habits among college students in an internet environment. Journal of Broadcasting & Electronic Media 50 (2), 193–210. Dimmick, J., Kline, S., Stafford, L., 2000. The gratification niches of personal e-mail and the telephone. Communication Research 27 (2), 227–248. Duguid, P., 1996. Material matters: the past and futurology for the book. In: Nunberg, G. (Ed.), The Future of the Book. University of California Press, Berkeley, pp. 63–102. Dutta-Bergman, M.J., 2004. Complementarity in consumption of news types across traditional and new media. Journal of Broadcasting & Electronic Media 48 (1), 41–60. Ferguson, D.A., Perse, E.M., 2000. The world wide web as a functional alternative to television. Journal of Broadcasting & Electronic Media 44 (2), 155–174. Flavian, C., Gurrea, R., 2007. Perceived substitutability between digital and physical channels: the case of newspapers. Online Information Review 31 (6), 793–813. Garcia, C., 2012. eMarketer predicts online ad trends in 2012: Hint-video is big. CMS Wire. (retrieved 09.05.12). Hair Jr., J.F., Anderson, R.E., Tatham, R.L., 1995. Multivariate Data Analysis, third ed. Macmillan, New York. Hall, A., 2005. Audience personality and the selection of media and media genres. Media Psychology 7 (4), 377–398. Hearn, G., 1989. Active and passive conceptions of the television audience: effects of a change in viewing routine. Human Relations 42 (10), 857–875. Henke, L., Donohue, T.R., 1989. Functional displacement of traditional TV viewing by VCR owners. Journal of Advertising Research 29 (2), 18–23. Holahan, C., 2007, November 20. Web video: Moreover, amateurs. (retrieved 13.07.11). Horton, C., 2012, June 27. Online video use exceeds 84%: Marketers rush to video ad spending. Business 2 Community. (retrieved 08.09.12). Jeffres, L.W., 1978. Cable TV and viewer selectivity. Journal of Broadcasting 22 (2), 166–177. Johnson, B., 2012, June 26. Top 100 advertisers boost ad spending but not in traditional media. Advertising Age. Jones, S., Johnson-Yale, C., Millermaier, S., Perez, F.S., 2009. Everyday, life, online: U.S. college students’ use of the Internet. First Monday, 14(10). (retrieved 08.09.12). Kaplan, S.J., 1978. The impact of cable television services on the use of competing media. Journal of Broadcasting 22 (2), 155–165. Kim, J., Rubin, A.M., 1997. The variable influence of audience activity on media effects. Communication Research 24 (2), 107–135. LaRose, R., 2010. The problem of media habits. Communication Theory 20 (2), 194–222. LaRose, R., Eastin, M.S., 2004. A social cognitive theory of Internet uses and gratifications: toward a new model of media attendance. Journal of Broadcasting & Electronic Media 48 (3), 358–377. Lazarsfeld, P.F., 1940. Radio and the Printed Page. Dell, Sloan, & Pearce, New York, NY. Lee, W., Kuo, E.C.Y., 2002. Internet and displacement effect: Children’s media use and activities in Singapore. Journal of Computer Mediated Communication 7 (2). (retrieved 10.03.12). Lehmann, D.R., 1971. Television show preference: application of a choice model. Journal of Marketing Research 8 (1), 47–55. Levy, M.R., 1987. VCR use and the concept of audience activity. Communication Quarterly 35 (3), 267–275. Levy, M.R., Windahl, S., 1984. Audience activity and gratifications. Communication Research 11 (1), 51–78. Mendelsohn, H., 1964. Listening to Radio. Free Press, New York, NY. Metzger, M.J., Flanagin, A.J., 2002. Audience orientations toward new media. Communication Research Reports 19 (4), 338–351. NB, 2008. US TV watching by age groups. ZDNet. (retrieved 04.09.12). Nicholson, W., 1995. Microeconomic theory: Basic principles and extensions. Dryden, New York. Nie, N.H., Simpser, A., Stepanikova, I., Zheng, L., 2005. Ten years after the birth of the Internet, how do Americans use the Internet in their daily lives? Stanford Institution for the Quantitative Study of Society, Stanford, CA. Nunberg, G., 1996. The Future of the Book. University of California Press, Berkeley. Papacharissi, Z., Rubin, A.M., 2000. Predictors of internet use. Journal of Broadcasting & Electronic Media 44 (2), 175–196. Parks Associates., 2010. Consumption of PC video is increasing, but TV still main video source for households. (retrieved 03.03.12). Perse, E.M., 1990a. Audience selectivity and involvement in the newer media environment. Communication Research 17 (5), 675–697. Perse, E.M., 1990b. Involvement with local television news: cognitive and emotional dimensions. Human Communication Research 16 (4), 556–581. Perse, E.M., 1998. Implications of cognitive and affective involvement for channel changing. Journal of Communication 48 (3), 49–65. Petty, R., Cacioppo, J., 1986. Communication and Persuasion: Central and Peripheral Routes. Springer-Verlag, New York. Phau, I., Poon, S.M., 2000. Factors influencing the types of products and services purchased over the internet. Internet Research: Electronic Networking Applications and Policy 10 (2), 102–113. Pingree, S., Hawkins, R.P., Hitchon, J.C.B., Gilligan, E., Radler, B., Kahlor, L., Gorham, B., Kolbeins, G.H., Schmidt, T., Kannaovakun, P., 2001. If college students are appointment television viewers. Journal of Broadcasting & Electronic Media 45 (3), 446–463. Potts, R., Dedmon, A., Halford, J., 1996. Sensation seeking, television viewing motives, and home television viewing patterns. Personality and Individual Difference 21 (6), 1081–1084. Preston, J.M., Clair, S.A., 1994. Selective viewing: cognition, personality and television genres. British Journal of Social Psychology 33 (3), 273–288. Prior, M., 2005. News vs. Entertainment: how increasing media choice widens gaps in political knowledge and turnout. American Journal of Political Science 49 (3), 577–592. Quantcast, 2012. Top sites for United States. (retrieved 14.01.12). Rosengren, K.E., Windahl, S., 1972. Media consumption as a functional alternative. In: McQuail, D. (Ed.), Sociology of Mass Communications. Penguin, Middlesex, England, pp. 166–194. Rubin, A.M., 1981. An examination of television viewing motivations. Communication Research 8 (2), 141–165. Rubin, A.M., 1983. Television uses and gratifications: Interactions of viewing patterns and motivations. Journal of Broadcasting 27 (1), 37–52.

200

J. Cha / Telematics and Informatics 30 (2013) 189–200

Rubin, A.M., 1984. Ritualized and instrumental television viewing. Journal of Communication 34 (4), 67–77. Rubin, A.M., Perse, E.M., 1987a. Audience activity and soap opera involvement. Human Communication Research 14 (2), 246–268. Rubin, A.M., Perse, E.M., 1987b. Audience activity and television news gratifications. Communication Research 14 (1), 58–84. Staff, E., 2010, July 14. Online video ad segment poised to explore. AdWeek. (retrieved 10.06.12). Survey, U., 2007. College students’ online video use dramatically surpasses general population’s. (retrieved 05.09.12). Tsao, J.C., Sibley, S.D., 2004. Displacement and reinforcement effects of the internet and other media as sources of advertising information. Journal of Advertising Research 44 (1), 126–142. Vicent, R.C., Basil, M.D., 1997. College students’ news gratifications, media use, and current events knowledge. Journal of Broadcasting & Electronic Media 41 (3), 380–392. Williams, F., Rice, R.E., Rogers, E.M., 1988. Adoption of new media. In: In Research Methods and the New Media. Free Press, pp. 70–90. Wober, J.M., Gunter, B., 1986. Exploring the entertainment experience. In: Bryant, J., Zillmann, D. (Eds.), Perspectives on Media Effects. Lawrence Erlbaum Associates, Hillsdale, NJ. Youn, S.-M., 1994. Program type preference and program choice in a multichannel situation. Journal of Broadcasting & Electronic Media 38 (4), 465–475. Zillman, D., Bryant, J., 1985. Selective Exposure to Communication. Erlbaum, Hillsdale, NJ.