• For Contributors +
• Journal Search +
Journal Search Engine
ISSN : 2287-1063(Print)
ISSN : (Online)
The Journal of Advertising and Promotion Research Vol.2 No.1 pp.87-121

# Factors Affecting Advertising Avoidance on Online Video Sites

Sejung Marina Choi, Ph.D.* , Eunice Kim, M.A.,Soojin Kim, M.A.,Yi-Hsin Yeh, Ph.D.
School of Media and Communication, Korea University
Department of Advertising, University of Texas at Austin,
University of Florida,Market Insights, Google, Inc.

### Abstract

The present study identified five antecedents of advertising avoidance– three dimensions of perceived value of advertising (i.e., information, entertainment,and incentive), perceived advertising intrusiveness, and attitudetowards advertising – on Hulu.com, a popular consumer website thatprovides streaming video of television programs and movies. Based on surveydata, the findings showed that perceived intrusiveness of advertisinghad the greatest impact on ad avoidance, followed by attitude towardsadvertising. In addition, a post‐hoc analysis explored the mediational relationshipsamong the variables.

JAPR_2-1_87.pdf313.6KB

### 1. INTRODUCTION

Ever since the introduction of online video sites, the media industry has witnessed its rapid growth and widespread popularity. Online video sites provide a wide range of video content such as movies, television shows and other user‐generated video clips. Some video sharing websites may charge fees for consumers to view content, but the large majority of them offer free services to their audiences. Online video sites can be loosely cate-gorized into two types: Sites that serve as platforms for user‐generated vid-eos like YouTube, and sites such as Hulu that focus on providing the online format of professional video content mostly for free (i.e., television dramas, trailers, or movies) (Jones 2009).

While advertisers hesitate to place ads on websites such as YouTube for the reason that they are unable to control the user‐generated content around which their ads are placed, they are relatively more optimistic about the potential of websites offering streaming videos of traditional media content such as television shows and movies as an effective advertising medium online. Especially with the explosive growth of online video sites like Hulu, the advertising budgets allocated for such websites also experi-enced a rapid increase accordingly.

Indeed, advertising spending on Hulu in 2008 was  approximately $70 million (Bradshaw and Garraha 2008) and four years later, Hulu's revenue grew to reach$695 million (Baker 2012). In addition, Hulu ranked no. 1 in video ads, generating more than 1.5 billion video ad impressions in the month of February, 2012. This number is even greater than the second and third biggest video ad networks combined during the same time frame (i.e., 1.1 billion video ads produced by Google including YouTube) (O'Malley 2012). Hulu also recorded the highest frequency of video ads during the month: On average, its viewers saw video ads 49 times (O'Malley 2012).

Despite the industry’s optimism about the sites providing professional online content as an advertising medium, consumers’ acceptance of adver-tising placements on such sites remains unknown. Past studies suggest that people are increasingly resistant to television commercials and thus they ignore the ads (Clancey 1994) or use that time to participate in another activ-ity (Krugman and Johnson 1991; Speck and Elliott 1997). Online advertis-ing such as banner ads and pop‐up ads are also viewed as annoying and irritating (Edwards, Li and Lee 2002). Due to the irritating aspects of Internet ads, people tend to avoid online advertising (Benway 1999; Cho and Cheon 2004).

As one of the few exploratory studies that investigate this new form of media, results of this study could enhance our understanding of consum-ers’ video viewing patterns in relation to their attitude towards and avoid-ance of online video advertising. Theoretically, given the antecedents iden-tified in this study, a more comprehensive model explaining viewers’ ad-vertising avoidance patterns can be put forth and further our current under-standing of advertising viewing and avoidance in the online environment. Findings of this study also provide strategic implications for the practi-tioners regarding how to improve the effectiveness of video advertising on online video sites.

### 2. THEORETICAL FRAMEWORK

Advertising avoidance, defined as "all actions by media users that differ-entially reduce their exposure to ad content," can be expressed by intentionally ignoring ads and shifting focus (Clancey 1994; Li, Edwards and Lee 2002), through other behaviors such as leaving a room (Abernethy 1991), or by me-chanical means such as using a remote control to fast‐forward the ads (Speck and Elliot 1997 p. 61). Television viewers can avoid advertising by switching channels and zapping television commercials (Heeter and Greenberg 1985). People’s tendency to remove television commercials from their attention is prominent during commercial breaks: they avoid advertising by muting and ignoring them, by leaving the room, or by doing other activities such as talking with others (Krugman and Johnson 1991; Speck and Elliott 1997). Moreover, ad‐avoidance technologies today such as digital video recorders (e.g., TiVo) allow television viewers to record programming while skipping over advertisements.

‘Banner Blindness’ refers to Internet users’ tendency to avoid anything that looks like banner display advertisements on the Web (Benway 1999) because they have little interest in the advertised product or the Web site the banners are placed on, and thus have no reason to click the  ads (Pagendarm and Schaumburg 2001). Or this avoidance could be attributed to that fact that banners are simply not noticed due to their periphery loca-tions on Web pages, or they are not recognized as providing useful in-formation to Internet users (Benway 1998, 1999; Benway and Lane 1998). Pop‐up ads, the prevalent format of advertising on the Internet, appear when entering or exiting a Web page, interrupting viewers’ activity on the website. When forced to view the pop‐up ads, Internet users may respond by avoiding the ads on the Internet by cognitive, behavioral, or mechanical means, including reactions such as closing or deleting the pop‐up windows (Edwards et al. 2002).

#### 2) Online Video Sites

Online video sites provide viewers with a wide range of choices with millions of videos online. While user‐generated online video sites like YouTube account for the largest share of the online video market, online video sites that offer professional online video content, such as Hulu, are the ones that catch the greatest advertisers’ attention (Jones 2009).

These online video sites which provide high‐quality streaming video services can be understood as extended media formats in which a television model is available on the Internet. This is supported by the fact that these websites, referred to as streaming television sites or streaming television technologies (Jones 2009), are the media channels within which a wide range of television genres are being positioned. The sites such as Hulu do not produce any professional online content on their own; instead, their businesses are centered on distributing the content produced and owned by media companies such as NBC Universal and News Corp through streaming via the website (Oruganti 2009).

According t o Nielsen (2009), the recorded number of viewers for such video sites was over 123 million or about 43% as many people watching television over the same period in late 2008. The online video population reached over 182 million who viewed nearly 40 billion online videos in November, 2012 (comScore 2012). Online video sites may be understood as a convergent channel for traditional television and the Internet. People view video content originally produced for television on such sites. Just like traditional television which offers the audience information and enter-tainment, these websites may also present such values for their viewers.

#### 3) Perceived Value of Advertising on Online Video Sites

The value of advertising, defined as “a subjective evaluation of the rela-tive worth or utility of advertising to consumers”, is a useful tool for evaluat-ing the effectiveness of advertising (Ducoffe 1995, p. 1). Previous findings suggested that many people exhibit a high likelihood to avoid advertising in both traditional and online media (e.g., Cho and Cheon 2004; Speck and Elliot 1997). However, when advertising messages are relevant to consum-ers’ needs, consumers find the value of advertising. Ducoffe (1995) be-lieved that advertising works effectively when value is exchanged between consumers and advertisers through advertising messages.

Thus, it is expected that both dimensions of perceived advertising val-ues will also have an impact on ad avoidance in the context of online video sites. Because the advertising is placed within the medium having both characteristics of traditional and Internet media, the existing value di-mensions of advertising may represent online video site users’ perception of advertising value. Hence, the following hypotheses are put forth:

H1: Perceived information value of ads will negatively influence ad avoidance on online video sites.

H2: Perceived entertainment value of ads will negatively influence ad avoidance on online video sites.

Although studies about advertising avoidance in the online environ-ment have been well established (Cho and Cheon 2004; Grant 2005), the mechanism underlying ad avoidance may vary across different types of web applications. Advertising on online video sites, as a new online adver-tising format, possesses some unique characteristics which may result in different value perceptions affecting ad avoidance when compared to other online advertising formats: They may offer viewers incentives.

H3: Perceived incentive value of ads will negatively influence ad avoid-ance on online video sites.

#### 4) Perceived Intrusiveness of Advertising on Online Video Sites

Ad intrusiveness is perceived when advertisements interfere with the goals of people and the idea of perceived intrusiveness was suggested to be one of the most influencing factors that lead to irritation and ad avoidance (Edwards et al. 2002). Based on the literature review, it seems that whether people perceive advertising as intrusive or not still plays a crucial role in affecting their ad avoidances in either traditional or online media.

Intrusiveness of advertising may enhance recall by allowing the adver-tising to interfere with people’s goals and increase the possibility of the content being processed. However, it may result in negative attitude for-mation (Ha 1996). When advertising interrupts consumers’ goals, it may generate undesirable outcomes such as aggravation and negative attitudes (Krugman 1983). People feel irritated by ads when they are overwhelmed by its content or when their senses are excessively stimulated (Aaker and Bruzzone 1985; Bauer and Greyser 1968). As a result, people come to avoid advertising, as a retreat from the source of annoyance (Kennedy 1971; Krugman 1983; Park and McClung 1986; Soldow and Principe 1981). These lead to the following supposition: perceived ad intrusiveness is pos-itively associated with ad avoidance.

H4:  Perceived ad intrusiveness will positively influence avoidance of ads on online video sites.

#### 5) Attitude towards Advertising on Online Video Sites

Attitude towards advertising has been one of the most important con-cepts in advertising and marketing research. Attitude, generally defined as “a learned predisposition to respond in a consistently favorable or un-favorable manner with respect to a given object” (Fishbein and Ajzen 1975, p. 6) is a basic underlying concept to understand an individual's attitude towards advertising − “a learned predisposition to respond in a consistently favorable or unfavorable manner toward advertising in general” (MacKenzie and Lutz 1989, p. 54). Attitude towards advertising is consid-ered as a major predictor of advertising effectiveness.

Social psychology theories such as the theory of planned behavior and the theory of reason action, suggest attitude is one of the significant pre-dictors of people’s behavioral intention (Fishbein and Ajzen 1975; Ajzen 1985, 1987). Hence, a person’s negative attitude towards an ad could lead to his or her intention to avoid the ad. This argument directly relates to the prior finding by Lee and Lumpkin (1992) that reported zipping and zapping are related to one's attitude toward television commercials, especially with regard to one’s belief that the advertising contains information. In Speck and Elliott’s study (1997), attitude towards advertising explained the high-est variance in ad avoidance behavior for print and broadcasting media. More recently, Kelly, Kerr and Drennan (2010) argued that advertising avoidance on a social networking site is a likely consequence of un-favorable consumer attitudes, which are derived from consumer distrust of advertising. Thus, the research reported above leads to the following hypothesis:

H5: Attitude towards advertising will influence positively ad avoidance on online video sites.

The theoretical analysis of advertising on online video sites in the con-text of media characteristics has allowed us to identify the factors influenc-ing site viewers’ ad avoidances. Based on our extant literature, we theorize that three dimensions of perceived value of advertising, perceived ad in-trusiveness, and attitude towards advertising function as antecedent con-structs that affect avoidance of advertising on online video sites. A con-ceptual model of this study, which includes the five research hypotheses with the predicted directions, is presented in [Figure 1].

Figure 1. Conceptual Model and Hypothesis

### 3. METHOD

#### 1) Participants

Data for the present study was collected in December, 2009. A total of 80 college students from a major southeastern university participated in this study via a paper‐and‐pencil survey. College students were deemed as appropriate for this study because 76% of young Internet users aged from 18 to 29 reported watching or downloading an online video (Pew Internet and American Life Project 2007). The participation in this study was en-tirely voluntary.

To rule out any confusion with regard to the unclear definition of ‘online video site,’ participants were asked to answer the survey based on their experiences on the Hulu website (http://www. Hulu.com). Hence, this study was limited to the students who had experience in using Hulu. The rationale for the choice of the Hulu website was that Hulu has become one of the most popular online video sites, offering video content through streaming. It has been often cited for its explosive growth in popularity (Kirkpatrick and Lashinsky 2008). Three out of 80 participants were drop-ped out of the present study due to their lack of experience in watching advertising on online video websites. A total of 77 participants’ responses were used in this study.

Of the 77 respondents, 41.6% were men and 58.4% were female, with an average age of 23 years old. The majority of respondents was upper‐level college (53.5%) and graduate students (33.8%). Among them, 39.0% clas-sified themselves as Caucasian, 37.7% as Asian or Asian American, 18.2% as Hispanic, and 3.9% as African American. On average, our respondents have used the Hulu website for 10.03 months, and 40.1% of them were regular website visitors (3.9% on a daily basis, and 36.2% on a weekly ba-sis). Comedy was the most watched television genre (51.2%), followed by drama (36.2%) and animation/cartoons (23.8%).

#### 2) Measures

Four constructs were examined in this study: perceived value of adver-tising, perceived ad intrusiveness, attitude towards ads, and ad avoidance. Scales for these constructs were adapted from existing literature and modi-fied to fit the current study. All items were measured with seven‐point Likert scales, ranging from 1 being “strongly disagree” to 7 being “strongly agree”, except for attitudes toward ads, which was assessed by using a seven‐point semantic differential scale.

Perceived value of advertising. This construct encompasses three di-mensions―perceived informativeness, entertainment, and incentive val-ues of ads. The perceived informativeness and entertainment value of ad-vertising was measured using Edward et al.’s scales (2002), which were originally developed by Ducoffe (1996). Both informativeness (helpful, important, informative, and useful) and entertainment values (attractive, enjoyable, entertaining, and fun to watch) were measured with four items (α = 0.90, M = 3.07, SD = 1.18 and α = 0.93, M = 3.33, SD = 1.41, re-spectively). Seven items measuring the incentive‐based value of advertis-ing were created for this study (α = 0.92, M = 5.33, SD = 1.13).

Perceived ad intrusiveness. The intrusiveness scale was developed by Edwards et al. (2002), consisting of  seven items — distracting, disturbing, forced, interfering, intrusive, invasive, and obtrusive (α = 0.94, M = 4.88, SD = 1.46).

Attitude towards advertising. Attitude toward advertising on Hulu.com was measured with five items: (a) good/bad; (b) pleasant/unpleasant; (c) favorable/unfavorable, (d) believable/ unbelievable; and (e) qualified /unqualified (Choi and Rifon 2002; MacKenzie and Lutz 1989) (α = 0.90, M = 3.96, SD = 1.23).

Ad avoidance. We adopted the ad avoidance scale from Cho and Cheon (2004). Five items from the original scale were selected and adjusted to fit the online video website setting. Two items were self‐created, which included: (a) minimized windows to avoid advertising on online video websites; (b) hid windows to avoid advertising on online video websites. The final seven‐item scale yielded relatively high reliability, α = 0.90, M = 4.37, SD = 1.28.

### 4. RESULTS

#### 1) Hypothesis Testing

A multiple regression analysis was performed to simultaneously test Hypotheses 1 through 5 regarding the influences of three dimensions of perceived value of advertising (i.e., informativeness, entertainment, and incentive values), perceived ad intrusiveness, and attitudes towards ads on ad avoidance. Tables 1 and 2 present the observed relationships between the five independent variables and the dependent variable, ad avoidance. Overall, the regression model was significant, F(5, 71) =19.09, p < .05, and the five independent variables explained 54.3% of the variance in the dependent variable.

<Table 1> Regression model of perceived ad value, perceived ad intrusiveness, and attitude towards ad on ad avoidance: Analysis of variance regression results.

Contrary to the original hypotheses, three dimensions of perceived val-ue of advertising―informativeness, entertainment, and incentive, did not have significant impact on ad avoidance, disconfirming H1, H2 and H3. The results showed that perceived ad intrusiveness was the most influential predictor in the model with a β coefficient of .464, t (71) = 4.760, p < .05.

Although the first three hypotheses were not supported by the results of multiple regression analysis, it could be speculated that the influences of the three dimensions of perceived advertising value were overshadowed by another stronger and more direct predictor in the model such as attitude towards ads. In other words, attitude towards ads might mediate the effect of perceived value of ads on ad avoidance, and thus, perceived value of ads might only exert an indirect influence on ad avoidance through attitude towards ads. To test this alternative explanation, the following post‐hoc analysis was conducted to examine the mediation effect of attitude towards ads on other independent variables in the model.

#### 2) Post‐Hoc Analysis: Attitude towards Advertising as a Mediator

The first regression equation indicated that both entertainment and in-centive dimensions of perceived value of ads positively influenced attitude towards the ads, β = .549, t (71) = 4.047, p < .05 and β = .316, t (72) = 3.334, p < .05, respectively. However, the informativeness dimension of per-ceived value of ads and perceived ad intrusiveness were not significant pre-dictors of attitude towards the ads. Together, this regression equation was significant, F(4, 72) = 23.953, p < .05, and explained 54.7% of the variance in attitude towards advertising.

<Table 4> Regression model of perceived value of ads and perceived ad intrusiveness on attitude towards ads: Multiple regression results, F(4,72) = 23.953, p<.05, R² = .547.

The second regression equation revealed a significant negative effect of the entertainment dimension of perceived value of ads on ad avoidance, β = ‐.374, t (72) = ‐2.677, p < .05, and perceived ad intrusiveness had a sig-nificant positive effect on ad avoidance, β = .469, t (72) = 4.695, p < .05. The informativeness and incentive dimensions of perceived value of ads did not have a significant influence on ad avoidance. Overall, the second equation model was significant, F(4, 72) = 21.516, p < .05, and accounted for 51.9% of variance in ad avoidance.

<Table 5> Regression model of perceived value of ads and perceived ad intrusiveness on ad avoidance: Multiple regression results, F(4, 72) = 21.516, p<.05, R² = .519.

Finally, the third equation (same as the original multiple regression for hypotheses testing, see Tables 1 and 2) showed that, when attitude towards ads was included as a predictor in the model, attitude towards ads sig-nificantly affected ad avoidance, β = ‐.260, t (72) = ‐2.197, p < .05, while the effect of the entertainment dimension of perceived value of ads de-creased, β = ‐.232, t (72) = ‐1.534, p > .05. However, another significant predictor in the second equation—perceived ad intrusiveness— did not decrease its significant influence when attitude towards ads was included in the third equation. Hence, the results provided partial support for the mediation explanation. The findings suggested attitude towards ads only mediated the effect of the entertainment dimension of perceived ad value on ad avoidance but not the effect of perceived ad intrusiveness.

### 5. DISCUSSION

Online video sites are one of the fastest growing segments of online media, and accordingly, they are considered by advertisers and marketers today as an attractive media channel for advertising placement. Unlike pre-vious online advertising formats, online video advertising has unique char-acteristics that could potentially attract viewers. However, little research has been done to examine the advertising acceptance of this newly‐devel-oped advertising format. Hence, the main objective was to identify factors that could account for viewers’ acceptance (or avoidance) of advertising on online video sites. Although this study is still in its exploratory stage, the findings of this study have made a theoretical and empirical con-tribution by identifying factors that impact people’s advertising avoidance behaviors when using online video sites.

Figure 2. A revised model of online video advertising avoidance in online video sites

On the other hand, perceived information values neither directly nor indirectly had an influence on viewers’ ad avoidance. This finding may have resulted from the potential influences that media context has on the advertising value. Advertising placed in a less credible medium such as television is considered to be less informative compared to media per-ceived as credible (Becker, Martino and Towners 1976). As online video sites such as Hulu are understood as a platform in which the traditional tele-vision model is applied to the video streaming service on the Internet (Jones 2009), the informational value of advertising on such websites may not be linked to avoidance of the ad.

### 6. LIMITATIONS AND FUTURE RESEARCH

This paper examines the antecedents of people’s avoidance of advertis-ing on online video sites by asking about their experiences in using the Hulu website. One of the limitations that this study has is whether the findings from Hulu can be generalized to all online video sites. As noted, there are online video sites like Hulu in which people watch highly qualified video programs offered by professional video service providers. On the other hand, online video sites such as YouTube provide a website platform in which amateur Internet users upload video content, including self‐created content. For future research, it may be informative to distinguish the two types of online video sites and to apply the current research framework to advertising on different types of such sites.

Future examination of factors that affect ad avoidance would be greatly enhanced by measuring people’s motivations for using online video sites. Given the basic theoretical background on viewers’ motivations for using online video websites, it is believed that people’s usage motives will help better explain the proposed conceptual framework. Users’ motivations af-fect their perceptions of advertising on online video sites, depending on the degree to which the advertising would support or interrupt in accom-plishing the motivations. Advertising on online video sites might be per-ceived as valuable by users if its existence is in accordance with their moti-vations of using the video site.  More research is needed to define the usage motivations associated with online video sites to provide empirical data with relation to the media use motivations.

The sample size was relatively small considering the growing number of online video site users. This study could have been more conclusive with a larger sample size. Also, it would be valuable to conduct an experimental study in the future that directly assesses people’s actual avoidance of adver-tising on online video sites in order to elaborate the definition of ad avoid-ance and the contributing factors.

### Reference

1.Aaker, David S. and Donald E. Bruzzone (1985), "Causes of Irritation in Advertising," Journal of Marketing, 49 (2), 47‐57.
2.Abernethy, Avery M. (1991), "Physical and Mechanical Avoidance of Television Commercials: An Exploratory Study of Zipping, Zapping and Leaving," in Proceedings of the American Academy of Advertising, Rebecca Holman, ed., New York: The American Academy of Advertising, 223‐231.
3.Ajzen, Icek (1985), "From intentions to actions: A theory of planned behavior," In J. Kuhi & J. Beckmann (Eds.), Action.control: From cognition to behavior, Heidelberg: Springer.
4._____ (1987), Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. In L. Berkowitz (Ed.), Advances in experimental social psychology, 20, New York: Academic Press.
5.Baker, Liana B. (2012), "Hulu Reveue 2012: Web TV Service Books \$695 Million," The Huffington Post (December 17, 2012). Retrieved March 2, 2013 from http://www.huffingtonpost.com/2012/12/17/hulurevenue-2012_n_2316831.html
6.Baron, Reuben M. and David A. Kenny (1986), "The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations," Journal of Personality & Social Psychology, 51, 1173‐1182.
7.Bauer, Raymond A. and Stephen A. Greyser (1968), Advertising in American: The ConsumerView? Boston, MA: Harvard University.
8.Becker, Lee B., Raymond A. Martino and Wayne M. Towers (1976), "Media Advertising Credibility," Journalism Quarterly, 53, 216‐22.
9.Benway, Jan P. (1998), "Banner blindness: The irony of attention grabbing on the World Wide Web," Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting, 1, 463‐467.
10._______ (1999), "Banner Blindness: What Searching Users Notice and Do Not Notice on the World Wide Web," Ph.D. dissertation, Rice University.
11._______, and David M. Lane (1998), "Banner blindness: Web searchers often miss 'obvious' links," internetworking, Retrieved April, 2, 2010, from http://www.internettg.org/newsletter/dec98/banner_blindness.html
12.Bradshaw, Tim and Matthew Garrahan (2008),"Rival forecast to catch YouTube," The Financial Times (November 16, 2008). Retrieved September 14, 2010, from http://us.ft.com/ftgateway/superpage. ft?news_id=fto111620081851222523
13.Chen, Qimei and William D. Wells (1999), "Attitude Toward the Site," Journal of Advertising Research, 39 (September/ October), 27‐37.
14.Choi, Sejung Marina and Nora J. Rifon (2002), "Antecedents and consequences of web advertising credibility: a study of consumer response to banner ads," Journal of Interactive Advertising, 3 (1). 91‐ 105
15.Clancey, Maura (1994), "The Television Audience Examined," Journal of Advertising Research, 39 (5), 27‐37.
16.Cho, Chang‐Hoan and Hongsik J. Cheon, (2004), "Why do people avoid advertising on the internet," Journal of Advertising, 33 (4), 89–97.
17.comScore (2012), "comScore Releases November 2012 U.S. Online Vide o Rankings," Retrieved January 19, 2013 from http://www.comscore.com/Insights/Press_Releases/2012/12/comScore_Releases_Nove mber_2012_U.S._Online_Video_Rankings
18.Diaz, Andrea Narholz, Kathy Hammond and Gil McWilliam (1997), "A Study of Web Use and Attitudes Amongst Novices, Moderate Users and Heavy Users," EMAC. 1624‐1635.
19.Ducoffe, Robert H. (1995), "How Consumers Assess the Value of Advertising," Journal of Current Issues and Research in Advertising, 17 (1), 1‐18.
20._______, Robert H. (1996), "Advertising Value and Advertising on the Web," Journal of Advertising Research, 36 (5), 21‐35.
21.Heeter, Carrie and Bradley S. Greenberg (1985), "Profiling Zappers," Journal of Advertising Research, 25 (2), 15‐19.
22.Edwards, Steven.M., Hairong Li and Joo‐Hyun Lee (2002), "Forced exposure and psychological reactance: Antecedents and consequences of the perceived intrusiveness of pup‐up ads," Journal of Advertising, 31 (3), 83‐95.
23.Fishbein, Martin and Icek Ajzen (1975), "Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research," Addison‐ Wesley, Reading, MA.
24.Grant, Ian C. (2005), "Young Peoples' Relationships with Online Marketing Practices: An Intrusion Too Far?" Journal of Marketing Management, 21 (5/6), 607‐623.
25.Ha, Louisa (1996), "Advertising Clutter in Consumer Magazines: Dimensions and Effects," Journal of Advertising Research, 36(July/August), 76‐83.
26.Hightower, Raymond T. (2008), "Hulu: Video & Effective Advertising", Retrieved March 20, 2010 from http://www.wisdomgroup.com/blog/hulu_video_and_effective_advertising/
27.Jones, Elisabeth. (2009), "Network Television Streaming Technologies and the Shifting Television Social Sphere," Paper presented Media in Transition 6: Stone and Papyrus, Storage and Transmission' Intern ational Conference 2009 Massachusetts Institute of Technology, Retrived March 24, 2010 from http://web.mit.edu.ezproxy.lib.utexa s.edu/commforum/mit6/papers/Jones.pdf
28.Kennedy, John R. (1971), "How Program Environment Affects TV Commercials," Journal of Advertising Research, 11 (1), 33‐38. .
29.Kelly, Louise, Gayle Kerr and Judy Drennan (2010), "Avoidance of Advertising in Social Networking Sites: the teenage perspective," Journal of Interactive Advertising, 10 (2), 16‐27.
30.Kirkpatrick, David and Adam Lashinsky (2008). "A New Way to Watch TV." Fortune, 157 (5), 33‐40.
31.Krugman, Herbert E. (1983), "Television Program Interest and Commercial Interruption: Are Commercials on Interesting Programs Less Effective?" Journal of Advertising Research, 23 (1), 21‐23.
32.Krugman, Dean M. and Keith F. Johnson (1991), "Differences in the Consumption of Traditional Broadcast and VCR Movie Rental," Journal of Broadcasting & Electronic Media, 35 (Spring), 213‐232.
33.LaRose, Robert, Matthew S. Eastin and Jennifer Gregg (2001), "Reformulating the internet paradox: social cognitive explanations of Internet use and depression," Journal of Online Behavior, 1 (2). Retrieved September 10, 2010 from http://www.behavior.net/JOB/v1n2/paradox.html
34.Lee, Seonsu and James R. Lumpkin (1992), "Differences in Attitude Toward TV Advertising: VCR Usage as a Moderator," International Journal of Advertising, 11 (4), 333‐342.
35.MacKenzie, Scott B. and Richard J. Lutz (1989), "An Empirical Examination of the Structural Antecedents of Attitude toward the Ad in an Advertising Pretesting Context," Journal of Marketing, 53 (April), 48–65.
36.Nielsen (2009), "A2/M2 Three Screen Report, 4th Quarter 2008: Television, Internet and Mobile Usage in the U.S.," The Nielsen Company. Retrieved March 28, 2010, from http://www.nielsenonline.com/downloads/3_Screens_4Q08_final.pdf.
37.O'Malley, Gavin (2012), "Hulu Tops in Video Ads," Media (March 16, 201 2). Retrieved March 2, 2013 from http://www.mediapost.com/publications/article/170381/#axzz2T4IoNzGu
38.Oruganti, Rama (2009), "Hulu, to be or not to be," Vincent L. Lacorte Case Studies Case #6‐0030. Center for Digital Strategies. Dartmouth College. Retrived September 19, 2010, from mba.tuck.dartmouth.edu/digital/Research/CaseStudies/6-0030.pdf
39.Park, C. Whan and Gordon W. McClung (1986), "The Effect of TV Program Involvement on Involvement with commercials," in Proceedings of Association of Consumer Research, Richard J. Lutz, ed., Las Vegas, NV: Association of Consumer Research, 544‐547.
40.Pasadeos, Yorgo (1990), "Perceived Informativeness of and Irritation with Local Advertising," Journalism Quarterly, 67 (1), 35‐39
41.Pagendarm, Magnus. and Heike Schaumburg (2001), "Why Are Users Banner‐ Blind? The Impact of Navigation Style on the Perception of Web Banners," Journal of Digital Information, 2(1), Retrieved October 30, 2009 from http://jodi.ecs.soton.ac.uk/Articles/v02/i01/Pagendarm/
42.Pew Research Center (2007), "Online Videos Go Mainstream", Retrieved November 20, 2009 from, http://pewresearch.org/pubs/552/onlinevideos- go‐mainstream
43.Rubin, Alan M., Elizabeth M. Perse and Robert A. Powell (1985), "Loneliness, parasocial interaction, and local television news viewing," Human Communication Research, 12, 155‐180.
44.Speck, Paul S. and Michael T. Elliott (1997), "Predictors of Advertising Avoidance in Print and Broadcast Media," Journal of Advertising, 26 (Fall), 61–76.
45.Soldow, Gary F. and Victor Principe (1981), "Response to Commercials as a Function of Program Context," Journal of Advertising Research, 21 (2), 59‐65.