Factors Influencing the Intention to Watch Online Video Advertising

Abstract

This study examines the factors influencing consumer intention to watch online video ads, by applying the theory of reasoned action. The attitude toward watching online video ads, the subjective norm, and prior frequency of watching online video ads positively influence the intention to watch online video ads. Further, beliefs held about entertainment and information outcomes from watching online video ads and subjective norm influence attitude toward watching these ads.

Introduction

In response to the increase in popularity of online videos among consumers, advertisers are showing a growing interest in using online videos as advertising tools.¹ By 2013, spending on online video advertising is estimated to reach $5.8 billion.^2,3 Unlike the forced exposure of consumers to traditional TV commercials and consumers' lack of control over presentation of the message, consumers can choose to view the online video ads with video playback controls (OVAs) when they want and also control the presentation of OVAs.⁴ Given that the viewing of OVAs requires active participation by consumers, it is important to examine the factors that influence consumer intention to watch these ads. The present study investigates those factors and examines the relationships between those factors. The theory of reasoned action (TRA)^5,6 will serve as the theoretical framework for this study.

Literature Review

Determinants of intention to watch OVAs

The TRA is a most useful theoretical framework for predicting a particular behavior.^5,6 According to the TRA, the most important determinant of a behavior is an individual's intention to perform the behavior.⁶ Further, the first determinant of behavioral intention is the attitude toward performing a behavior, which refers to “the individual's positive or negative evaluation of performing the behavior”.⁵^(p6) Previous studies have shown that there is significant influence of the attitude toward a behavior on the intention to perform that behavior.^7–9 In the context of OVAs, therefore, a more favorable attitude toward watching OVAs is expected to lead to a stronger intention to watch these ads. Thus:

H1: An individual's attitude toward watching OVAs will positively relate to the intention to watch the ads.

According to the TRA, the second determinant of behavioral intention is subjective norm, which refers to “the person's perception that most people who are important to him think he should or should not perform the behavior in question”.⁶^(p302) Previous studies have found that the stronger the subjective norm is, the stronger the behavioral intention will be.^10,11 Similarly, consumer perceived social pressure to watch OVAs will have a positive effect on consumer intention to watch those ads. Thus:

H2: Subjective norm with respect to watching OVAs will positively relate to intention to watch the ads.

Although the original TRA assumes that there are independent influences of the attitude toward a behavior and the subjective norm on intention, respectively, some researchers suggest that the interdependencies between the two main determinants of behavioral intention should be examined to improve the overall explanatory power of the model.^11–13 Consistent with previous studies,¹³ subjective norm with respect to watching OVAs can be predicted to positively influence consumer attitudes toward watching those ads. Thus:

H3: Subjective norm with respect to watching OVAs will positively relate to attitude toward watching those ads.

Although not included in the original TRA, it was found that adding a past behavior as a co-determinant of behavioral intention to the original model strengthened the explanatory power of the model.^14,15 Therefore, in the context of OVAs, it is expected that the more frequently consumers watched OVAs in the past, the greater will be their intention to watch the ads. Thus:

H4: Prior viewing of OVAs will positively relate to the intention to watch such ads.

Determinants of attitude toward a behavior

The TRA assumes that the attitude toward a behavior is determined by behavioral beliefs, which refer to a “person's beliefs that the behavior leads to certain outcomes and his evaluations of these outcomes”.⁵^(p8) Although the original TRA assumes a unidimensional belief, researchers have decomposed beliefs to avoid underestimating the complicated relationship between beliefs and determinants of intention.^8,9,13,16

Given that OVAs are a new form of advertising on the Internet and motivations reflect certain expected outcomes for a particular behavior,¹⁷ a review of the literature on the motivations for watching advertising as well as for using the Internet is helpful in identifying the expected outcomes from viewing OVAs. In the context of advertising viewing, O'Donohoe¹⁸ identifies several motivations for watching advertising, including marketing uses, enjoyment, and social interaction. Similarly, Pollay and Mittal¹⁹ found that consumers use advertising for product information, social role guidance, and hedonic experiences. Additionally, in the context of Internet usage, a number of researchers have identified the specific gratifications that consumers expect to gain from using the Internet, including information,^20–22 entertainment,^21–23 social interaction,^20,22,23 relaxation-escape,²³ passing time,^21,23 interactive control,²⁰ and shopping.²⁴

Among the expected outcomes for using advertising and for using the Internet, the outcomes most relevant to the context of watching OVAs include entertainment, information, relaxation, escape, passing time, social interaction, and control. We predict that consumers will expect these same outcomes from watching OVAs. As consumers further believe that watching OVAs will produce these outcomes and judge those outcomes to be good outcomes, it is expected that consumers will develop a more positive attitude toward viewing OVAs. Thus:

H5: Each of the behavioral beliefs for watching OVAs—(a) entertainment; (b) information; (c) relaxation; (d) escape; (e) passing time; (f) social interaction; and (g) control—will positively relate to the attitude toward viewing the ads.

Methodology

Participants

An online survey was conducted. Overall, 514 undergraduate students at a large Midwestern university completed this survey in return for extra credit in their courses. Responses from 22 of the participants were removed because of many missing responses. Thus, responses from 492 participants were used for the subsequent analysis. Ages of the participants ranged from 18 to 29 (M=20.45, SD=1.50). Female participants (67.7 percent) outnumbered male participants. College students were deemed to be appropriate for this study. Prior research reported that about 89 percent of Internet users aged 18–29 watched online videos on video-sharing sites in 2009.²⁵

Measurement

Intention to watch OVAs and subjective norm were measured, using three items, respectively. Each item was assessed on a scale ranging from “strongly disagree (1)” to “strongly agree (7).” Attitude toward watching OVAs was measured using five items on a 7-point scale. The items used to measure each of these variables were taken from previous studies,^5,26 and modified for this study. Additionally, to measure prior viewing of OVAs, participants were asked to indicate how often they had watched OVAs in the past 3 months, on a scale ranging from “never (1)” to “quite often (7).”

With respect to behavioral beliefs, to gain better insight into what consumers expect from watching OVAs, three focus groups were conducted. A total of 19 undergraduate students (different students from the survey participants) each participated in one of three focus groups in a campus focus group room. These focus groups identified several expected outcomes from watching the OVAs: Entertainment, relaxation, escape, passing time, social interaction, and control. The review of previous studies and the findings in the focus groups, taken together, identified seven expected outcomes from watching OVAs, that is, entertainment, information, relaxation, escape, passing time, social interaction, and control. The strength of each belief and the evaluation of its corresponding outcome were measured using items borrowed from previous studies,^20,23,a after being modified for this study.⁵ The strength of each behavioral belief was assessed on a scale ranging from “extremely unlikely” (1) to “extremely likely” (7). Its corresponding evaluation was measured on a scale ranging from “extremely bad” (1) to “extremely good” (7). To compute the sum of the behavioral beliefs, the strength of each belief was multiplied by its corresponding evaluation, and the resulting scores were then summed for each outcome.⁵ See Appendix A for the items used in this study.

Results

Measurement model

Confirmatory factor analysis was conducted, using AMOS 7.0, to confirm the measurement model. Two indicators of the social interaction belief and two of the entertainment belief were removed due to cross loading. All of three indicators of the control belief were also removed due to a low level of composite reliability. The overall fit for the modified measurement model was acceptable (Table 1).²⁷ All indicators were loaded on the same constructs found in the previous literature. Composite reliabilities and average variances extracted for each of the constructs indicated convergent validity (Table 2).^12,28 Further, the square root of the average variance extracted for each construct was higher than the correlations involving the construct (Table 3), confirming discriminant validity.^12,29

Table 1.

Confirmatory Factor Analysis Results

	Unstandardized loading	Standardized loading	R ²
Behavioral belief items (Σ b_ie_i)
Factor 1: Entertainment
Entertainment 1	9.09	0.88	0.765
Entertainment 2	8.61	0.83	0.685
Entertainment 3	7.93	0.81	0.662
Factor 2: Information
Information 1	8.40	0.85	0.729
Information 2	7.99	0.82	0.674
Information 3	8.60	0.81	0.650
Information 4	8.30	0.77	0.585
Information 5	6.96	0.73	0.528
Factor 3: Relaxation
Relaxation 1	9.00	0.85	0.721
Relaxation 2	8.63	0.82	0.670
Relaxation 3	8.39	0.81	0.660
Relaxation 4	8.57	0.81	0.655
Factor 4: Escape
Escape 1	8.61	0.99	0.972
Escape 2	5.65	0.56	0.315
Factor 5: Passing time
Passing time 1	10.15	0.89	0.784
Passing time 2	9.44	0.85	0.715
Passing time 3	8.85	0.84	0.700
Passing time 4	7.68	0.71	0.506
Factor 6: Social interaction
Social interaction 1	10.26	0.92	0.849
Social interaction 2	9.40	0.87	0.748
Attitude items
Attitude 1	0.96	0.86	0.740
Attitude 2	0.91	0.84	0.711
Attitude 3	1.08	0.83	0.680
Attitude 4	1.13	0.82	0.675
Attitude 5	0.88	0.81	0.654
Subjective norm items
Subjective norm 1	1.41	0.93	0.864
Subjective norm 2	1.37	0.93	0.866
Subjective norm 3	1.19	0.69	0.474
Intention items
Intention 1	1.60	0.96	0.919
Intention 2	1.54	0.90	0.812
Intention 3	1.33	0.85	0.726

χ²(398)=1090.287, p<0.001; χ²/df=2.739; RMSEA=0.060; NFI=0.916; CFI=0.944; SRMR=0.041.

CFI, comparative fit index; NFI, normed fit index; RMSEA, root-mean-squared error of approximation; SRMR, standardized root mean of the residual.

Table 2.

Composite Reliabilities and Average Variance Extracted

Constructs	Composite reliability	Average variance extracted
Entertainment	0.878	0.706
Information	0.895	0.633
Relaxation	0.893	0.677
Escape	0.740	0.597
Passing time	0.893	0.680
Social interaction	0.889	0.799
Attitude	0.916	0.690
Subjective norm	0.880	0.709
Intention	0.933	0.823

Table 3.

Correlation Between Constructs

	ENT	INF	REL	ESC	PT	SI	ATT	SN	INT
ENT	0.840^*
INF	0.687	0.796^*
REL	0.702	0.529	0.823^*
ESC	0.412	0.243	0.480	0.773^*
PT	0.803	0.624	0.780	0.500	0.825^*
SI	0.591	0.410	0.696	0.491	0.616	0.894^*
ATT	0.711	0.600	0.579	0.376	0.617	0.376	0.831^*
SN	0.314	0.294	0.474	0.344	0.280	0.344	0.489	0.842^*
INT	0.639	0.566	0.588	0.367	0.561	0.367	0.723	0.580	0.907^*

The numbers in the diagonal row arc square roots of the average variance extracted.

ENT, entertainment; INF, information; REL, relaxation; ESC, escape; PT, passing time; SI, social interaction; ATT, attitude toward watching online video ads; SN, subjective norm; INT, intention to watch online video ads.

Hypotheses testing

To test the hypotheses, a path analysis was conducted, using AMOS 7.0. The control belief was not included in the analysis due to the low reliability, and thus H5g was not tested. To improve the overall fit of the hypothesized model, a path from the entertainment belief to intention to watch OVAs was added to the hypothesized model, after reviewing modification indices. The overall fit for the modified model was good (see Fig. 1).²⁷

FIG. 1.

Hypotheses Testing and Modified Model.

As shown in Figure 1, attitude toward watching OVAs, subjective norm, and prior viewing of OVAs all had a significant positive relationship to the intention to watch OVAs. Additionally, subjective norm showed a positive impact on attitude toward watching OVAs. Hence, H1–H4 were all supported. Further, entertainment and information both had a positive impact on attitude toward watching OVAs. However, other behavioral beliefs all showed no significant relationship to attitude. Thus, H5a and H5b were both supported, whereas H5c–f were not. Finally, there was a positive impact of entertainment on intention to watch OVAs.

Discussion and Conclusions

Our findings demonstrate that the TRA is a robust theory that can help researchers predict the factors that influence consumer intention to watch a subset of advertising—online video ads—which requires consumers' active participation. Specifically, this research found that the more positive the participant attitudes toward watching OVAs were and the more that participants perceived there was social pressure to watch those ads, the greater was their intention to watch these ads. Additionally, subjective norm was found to influence attitudes toward watching OVAs positively. Advertisers may consider partnering with video sharing Web sites and social network sites, so consumers can see whether their friends have viewed OVAs. Such a feature will lead consumers to form favorable attitudes toward watching OVAs and subsequently motivate them to watch those ads. This study also found that the intention to watch OVAs is also influenced by the habitual¹⁰ watching of consumers.

Building upon the motivations for viewing advertising and for using the Internet, as identified in previous studies, this study identified six outcomes that consumers expect from watching OVAs: Entertainment, information, relaxation, escape, passing time, and social interaction. Understanding these types of outcomes gives useful insights to advertisers for how they can best satisfy consumers' specific needs for viewing OVAs. Specifically, this study found that the more that participants believed that watching OVAs would entertain them and the more they judged that outcome to be a good outcome, the more positive were their attitudes toward watching OVAs. Additionally, this behavioral belief about entertainment positively influenced consumer intention to watch OVAs directly. This study suggests that OVAs should be very entertaining to enhance consumer attitudes toward watching OVAs the most and increase their intention to watch these ads. Additionally, given that the belief about information outcome also positively influenced consumer attitudes toward watching OVAs, entertaining OVAs should provide useful information at the same time.

Unexpectedly, however, other behavioral beliefs for watching OVAs did not predict their attitudes toward watching the ads. These nonsignificant findings might have occurred because participants' beliefs about these outcomes were not strong enough to influence their attitudes toward watching OVAs. These findings are consistent with the findings of previous studies demonstrating that some motivations for using the Internet such as passing time and relaxation did not predict Internet affinity and satisfaction.^21,23

Limitations and Future Research

Although the list of expected outcomes from watching OVAs that were identified in this study is comprehensive, it is not exhaustive. Additionally, this study examined the factors identified in the TRA. Future research should identify a more comprehensive list of expected outcomes for watching OVAs and examine other factors that may further predict consumer intention to watch OVAs.

Endnote

^aSeveral items used in these studies were not included in this study because some of these items do not reflect consequences in general and other items do not reflect a specific outcome that they are supposed to represent.

Footnotes

Disclosure Statement

No competing financial interests exist.

Appendix A: Items Used in Scales

a

These items were borrowed from Ferguson and Perse.²³

b

These items were borrowed from Korgaonkar and Wolin.²⁰

*

These items were deleted.

References

Beal

. 2007. Can online video advertising really reach $4.3B by 2011? Marketing Pilgrim 2007; July 16. www.marketingpilgrim.com/2007/07/can-online-video-advertising-really-reach-43b-by-2011.html. 2007 Aug 2.

eMarketer. 2008a. Video advertising growth factors. www.emarketer.com/Reports/All/Emarketer_2000537.aspx. 2008 Dec. 10.

eMarketer. 2008b. Video advertising online: spending and pricing. www.emarketer.com/Reports/All/Emarketer_2000536.aspx. 2008 Dec. 10.

Southgate

, Westoby

, Page

. Creative determinants of viral video viewing. International Journal of Advertising, 2010; 29:349–368.

Ajzen

, Fishbein

. 1980. Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

Fishbein

, Ajzen

. 1975. Belief, attitude, intention and behavior: an introduction to theory and research. Reading, Massachusetts: Addison-Wesley.

Fitzmaurice

. Incorporating consumers' motivations into the theory of reasoned action. Psychology & Marketing, 2005; 22:911–929.

Ramayah

, Rouibah

, Gopi

et al. A decomposed theory of reasoned action to explain intention to use Internet stock trading among Malaysian investors. Computers in Human Behavior, 2009; 25:1222–1230.

Oliver

, Bearden

. Crossover effects in the theory of reasoned action. Journal of Consumer Research, 1985; 12:324–340.

10.

Bagozzi

, Wong

, Abe

et al. Cultural and situational contingencies and the theory of reasoned action: application to fast food restaurant consumption. Journal of Consumer Psychology, 2000; 9:97–106.

11.

Choo

, Chung

, Pysarchik

. Antecedents to new food product purchasing behavior among innovator groups in India. European Journal of Marketing, 2004; 38:608–625.

12.

Bock

, Zmud

, Kim

et al. Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 2005; 29:87–111.

13.

Ryan

. Behavioral intention formation: the interdependency of attitudinal and social influence variables. Journal of Consumer Research, 1982; 9:263–278.

14.

Albarracín

, Wyer

. The cognitive impact of past behavior: influences on beliefs, attitudes, and future behavioral decisions. Journal of Personality and Social Psychology, 2000; 79:5–22.

15.

Kidwell

, Jewell

. The influence of past behavior on behavioral intent: an information-processing explanation. Psychology & Marketing, 2008; 25:1151–1166.

16.

Taylor

, Todd

. Decomposition and crossover effects in the theory of planned behavior: a study of consumer adoption intention. International Journal of Research in Marketing, 1995; 12:137–155.

17.

Lin

. 1999. Uses and gratifications. Stone

, Singletary

, Richmond

. Clarifying communication theories: a hands-on approach. Ames, Iowa: Iowa State University Press, 199–208.

18.

O'Donohoe

. Advertising uses and gratifications. European Journal of Marketing, 1994; 28:52–75.

19.

Pollay

, Mittal

. Here's the beef: factors, determinants, and segments in consumer criticism of advertising. Journal of Marketing, 1993; 57:99–114.

20.

Korgaonkar

, Wolin

. A multivariate analysis of web usage. Journal of Advertising Research, 1999; 39:53–68.

21.

Papacharissi

, Rubin

. Predictors of Internet use. Journal of Broadcasting and Electronic Media, 2000; 44:175–196.

22.

, Cho

, Roberts

. Internet uses and gratifications: a structural equation model of interactive advertising. Journal of Advertising, 2005; 34:57–70.

23.

Ferguson

, Perse

. The world wide web as a functional alternative to television. Journal of Broadcasting and Electronic Media, 2000; 44:155–174.

24.

Rodgers

, Sheldon

. An improved way to characterize Internet users. Journal of Advertising Research, 2002; 42:85–94.

25.

Pew Internet & American Life Project. 2009. The audience for online video-sharing sites shoots up. www.pewinternet.org/Reports/2009/13—The-Audience-for-Online-VideoSharing-Sites-Shoots-Up/2-Demographics.aspx?r=1. 2010 Jul. 2.

26.

Ajzen

. 2002, 2006Constructing a TPB questionnaire: conceptual and methodological considerations. www.people.umass.edu/aizen/pdf/tpb.measurement.pdf. 2008 Apr. 6.

27.

Kline

. 2005. Principles and practice of structural equation Modeling. NY: Guilford Press.

28.

Hair

, Black

, Babin

et al. 2006. Multivariate data analysis, 6th. Upper Saddle River, NJ: Prentice Hall.

29.

Fornell

, Larcker

. Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 1981; 18:382–388.