CYBERPSYCHOLOGY, BEHAVIOR, AND SOCIAL NETWORKING Volume 18, Number 3, 2015 ª Mary Ann Liebert, Inc. DOI: 10.1089/cyber.2014.0543
An Exploration of Motivations for Two Screen Viewing, Social Interaction Behaviors, and Factors that Influence Viewing Intentions Hongjin Shim, PhD,1 Poong Oh,2 Hyunjin Song,3 and Yeonkyung Lee 4
Abstract
This study explores whether, and how, motivations for two screen viewing predicted social interaction behaviors and subsequent viewing intention of TV programs. A total of 453 respondents who responded that they use social networking sites (SNSs) via smartphones and actively watch entertainment programs completed an online survey questionnaire. In agreement with uses and gratifications assumptions, motivations for TSV predicted distinctive sets of social interaction behaviors, which mediated the influence of motivations on viewing intentions. Respondents’ two screen viewing was meaningfully related with social interaction, engagement with programs, information seeking, and passing time. Results suggest that two screen viewing could provide shared experiences nourishing social capital and reintegrate TV audiences by social adhesive resulting from TV with SNSs.
Introduction
S
ocial and communication relations are likely to develop between those who share common interests and experiences.1 Since TV became available as a primary medium to the public, it has served as an important source of common interests and experience, functioning as ‘‘social adhesive’’2 and ‘‘water coolers.’’3 However, the drastic change in the media environment from the 1980s, in particular the increase in the number of channels, led to audience fragmentation. People tend to expose themselves selectively only to what they want to watch and thus can hardly share their interests and experiences with those who have different preferences and tastes. As a result, people are unlikely to develop common interests and experiences by watching TV in a multichannel environment. Recently, however, the social function of TV as a source of common interests and experiences has gained renewed attention, as social networking sites (SNSs) emerge as alternative communication channels, specifically among young adults. SNS users freely exchange their opinions of TV programs that they watch, which enhances the social interactions among SNS users who watch the same TV programs. This implies that SNSs can reintegrate TV viewers, allowing them to share their interests and experiences.4
It is worth noting that social interactions among TV viewers often occur through SNSs. According to Johns,5 people often read and leave messages about TV programs on SNSs using secondary devices, such as mobile phones and tablet PC. In other words, TV viewers look at two or more screens at the same time: one is a TV screen used to watch the TV program, and the other is a secondary device used as the backchannel for social interaction through SNSs. This new TV viewing behavior is called ‘‘two screen viewing’’ behavior (TSV).5 The primary goals of this study are twofold: (a) to explore the reasons why viewers are participating in these social interaction behaviors, and (b) to investigate the effects of such social interaction behaviors on TSV. Whereas many studies have been conducted on SNSs focusing on social interactions taking place on them,6,7 little scholarly attention has been paid to the potential antecedents and effects of TSV. Further, it has not been answered to what extent TSV promotes people to share common interest or experiences, thereby reintegrating TV viewers and empowering people to generate meanings from TV programs that are not intended by the producers.8 From this perspective, the present study investigates the antecedents and implications of TSV by exploring (a) motivations for TSV, (b) social interaction behaviors (SIB) that TSV leads to, and (c) their relationships with TV viewing intentions as one of the effects of TSV.
1
Broadcasting Media Research Division, Korea Information Society Development Institute, Jincheon-gun, Korea. Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, California. School of Communication, The Ohio State University, Columbus, Ohio. 4 School of Communication, Yonsei University, Seoul, Korea. 2 3
158
TWO SCREEN VIEWING ON THE RISE
SNSs are a key component of TSV functioning as a space for people to share their thoughts or opinions as well as common interest on TV programs. To understand motivations for TSV, it is necessary first to recognize motivations for SNSs. Previous research has identified several motivations for SNSs, which include self-expression, entertainment, passing time,9 relationship maintenance,10 and social interaction.11 More recently, co-viewing has been pointed out as an additional motivation for the use of YouTube.12 The motivations of entertainment and passing time10 have generally been recognized as major motivations for SNSs use as well. Haridakis and Hanson12 maintained that social interaction is another important motivation for the use of SNSs, through which people with common interests are connected.13,14 Therefore, based on the above literature on the motivations for media use, the following hypothesis is presented: H1: The main motivations for TSV will be social interaction, information seeking, and passing time.
Previous studies suggest that media use motivations are closely related to media selection.12, 15 Within the context of SNS use, different motivations result in various communication behaviors, for instance status updates on Facebook and real time chat.9 In the same way, motivations for TSV could affect behavior related to SIB. A study by McPherson et al.16 suggests that motivations for watching TV programs are significantly associated with sharing opinions about TV programs on Twitter. It was expected that a substantial relationship between motivations for TSV and SIB would exist. SIB is multidimensional,15 as people are variably active along several dimensions in the media use process.17,18 Thus, this study takes a different approach to SIB, in that two types of SIB were differentiated: receptive or expressive behaviors. In this context, this study proposes the following hypothesis: H2: The motivations of social interaction, information seeking, and passing time for TSV will be associated with receptive or expressive behaviors on SNSs.
An increasing number of mobile phone users use their phones to communicate with others while watching TV5 via SNSs that are readily available via smartphones. The SIB in SNSs among users serves as a backchannel, in which people can express both their opinions and emotions,19 or exchange the information about specific TV programs. Talking and obtaining information about particular TV programs satisfies people’s needs to share, seek, or even enjoy their thoughts, emotions, or opinions. Further, many studies have long supported that watching TV increases intention to watch specific program content, attention to program content, and cognitive and affective involvement with programs.8,15,20 These viewing behaviors are likely to boost intention to view or exposure to TV content mentioned by others at a later time.21 Given the motivations are one of the significant predictors of future TV viewing intention, SIB can also become a vehicle leading to intention to view. Thus, this study proposes the following hypotheses: H3: Intention to view can be predicted by the motivations of social interaction, information seeking, and passing time for TSV. H4: Receptive or expressive behaviors will mediate the relationship between TSV users’ motivations and intention to view.
159 Methods Data
The present study analyzed self-reported survey data collected from a random sample of 453 respondents who reported that they use SNSs via smartphones and actively watch entertainment programs for 5 hours or more per week.a The focus was on entertainment programs in particular because such programs enable viewers to experience ‘‘real’’ worlds vicariously through observations of others’ trials and tribulations,22 driving social viewing behaviors. The survey was conducted between March 10 and 18, 2013, in the metropolitan Seoul area of South Korea, and was administered online. Measures Motivations for TSV use. A scale tapping respondents’ motivations for using TSV was constructed following previous studies.23–25 Respondents were asked how likely they were to engage in TSV use on16 items (e.g., ‘‘to talk with others to share feelings on TV programs’’), each of which were measured on a 7-point scale (‘‘not at all likely’’ to ‘‘extremely likely’’). Preliminary analysis using exploratory factor analysis (principle component analysis using promax rotation with Kaiser normalization) revealed three motivations that drive TSV use. Social interaction behaviors on SNSs. The Interactional Behaviors Scale9,26 was adapted to gauge audience activities as SIB. The respondents were asked to answer 16 questions pertaining to how often they engaged in specific SIB on SNSs, using a 7-point scale (‘‘not at all’’ to ‘‘very often’’). Expressive behaviors were measured using 11 items, including ‘‘How frequently do you express emotions about TV programs on SNSs?’’ (Cronbach’s a = 0.95, M = 3.94, SD = 1.42). Receptive behaviors were measured with five items, including ‘‘How often do you watch streams of comments posted by others?’’ (Cronbach’s a = 0.88, M = 4.70, SD = 1.20). Viewing intentions. The respondents were asked three questions about the likelihood of watching entertainment programs based on a 7-point scale (‘‘not at all’’ to ‘‘extremely likely’’). Using the responses, a summary scale of viewing intention toward entertainment programs (three items; Cronbach’s a = 0.95, M = 5.6, SD = 1.07) was constructed. Demographic controls. All analyses included three demographic variables as control variables. The majority were female (51.1%). Age ranged from 21 to 64 years (M = 36.08 years, SD = 10.49 years). The sample tended to be highly educated; 82.3% of total respondents had a college degree. Analysis strategy
Exploratory factor analyses (using principal components analysis with promax rotation) were performed for the first H1. In testing simple mediation effects (H2–H4, as shown in Figure 1), the PROCESS macro27 was used to estimate a series of bootstrapped resampling-based regression models, which yields more valid and statistically powerful results than its alternatives.27 Across the analyses, heteroskedasticity-consistent standard errors and bias-corrected maximum likelihood
160
SHIM ET AL.
FIG. 1.
Hypothesized model.
confidence intervals were estimated using a total of 5,000 bootstrapping-based resamples, controlling for covariates for both the focal mediators and the dependent variable, entertainment program viewing intention. Results Motivations for TSV
Table 1 summarizes the results of factor analysis. Factor 1, social interaction, was comprised of eight items relevant to sharing an activity, such as expressing emotions about the program, sharing opinions about the program, or communicating with others (Cronbach’s a = 0.91, M = 4.72, SD = 1.08, explained variance = 47.51%). Of the five items for factor 2, engagement with entertainment programs/information seeking loaded most strongly on this factor. This related to involvement in reality shows and information gained from programs. In addition, items tapping the extent to which the respondents’ perceived an ability to influence programming by commenting on a program also loaded highly on this factor (Cronbach’s a = 0.86, M = 4.31, SD = 1.21, explained variance = 7.70%). Of the two items for factor 3, passing time was comprised of items tapping TSV as a tool to pass the time by watching TV (Pearson’s r = 0.81, M = 4.18, SD = 1.39, explained variance = 4.81%). The three motivation factors accounted for 60.00% of the total variance. Among the three motivation factors that were identified, social interaction had the highest mean scores, whereas en-
gagement with entertainment programs/information seeking and passing time were slightly less salient for TSV use. The findings suggest that respondents engage in TSV primarily to share their emotions or opinions, rather than to obtain information about the program or to seek enjoyment. Except for engagement with entertainment programs, motivations for TSV use in this study were partly identified in prior studies: passing time, entertainment, habit, companionship, information learning, escape, self-expression, identification, and social interaction.9,28,29 Therefore, H1 is partially supported. Relationships among motivations, SIB, and viewing intention
The first two columns in Table 2 show that all three of the motivations were found to be significant predictors of respondents’ actual SIB, receptive and expressive behaviors. Social interaction positively predicted receptive behavior (b = 0.422, SE = 0.08, p < 0.01), but not expressive behavior (b = 0.055, SE = 0.09, p > 0.05) above and beyond the effect of other control variables. In contrast, the opposite pattern was found for engagement with entertainment programs/ information seeking in that the motivation did not predict receptive behavior (b = 0.081, SE = 0.06, p > 0.05) but significantly correlated with expressive behavior (b = 0.579, SE = 0.07, p < 0.001). Lastly, passing time was significantly correlated with both receptive behavior (b = 0.084, SE = 0.04, p < 0.05) and expressive behavior (b = 0.164, SE = 0.48, p < 0.01) examined in this study, although the magnitude of the coefficient for passing time was somewhat smaller than that of social interaction, engagement with the program, or information seeking. H2 is therefore also supported. The third and fourth columns of Table 3 show the results of the simple mediation analysis, which suggest that the direct effects of social interaction (b = 0.227, SE = 0.06, p < 0.001) and engagement with entertainment programs/ information seeking (b = –0.129, SE = 0.05, p < 0.05) were significant predictors of intention to watch entertainment programs, supporting H3.
Table 1. Primary Factor Loadings of Motivations for Using Two Screen Viewing
To watch TV together To share my opinion on TV programs with others To find out something related to TV programs To chat away with friends while watching TV To have fun To find out others’ views and thoughts To interact with others To socialize with others To share a sense of realism and suspense with others To help keep programs I like on the air for many years to come To put across my views or opinions to producers of programs To boost the TV ratings of my favorite program To get a lot of information about programs I like To acquire new information about various programs Feel bored To pass the time by watching TV Eigenvalue % Variance explained Cronbach’s a
Factor 1
Factor 2
Factor 3
0.526 0.858 0.743 0.692 0.668 0.639 0.835 0.641 0.533 0.281 - 0.118 - 0.071 0.231 0.122 0.044 - 0.063 7.601 47.51% 0.91
0.292 0.024 0.100 - 0.091 - 0.158 0.144 - 0.037 0.062 0.259 0.564 0.797 0.787 0.544 0.686 - 0.056 0.183 1.231 7.70% 0.86
0.020 - 0.110 - 0.098 0.156 0.282 - 0.134 0.010 0.099 0.017 - 0.025 0.056 0.132 - 0.013 - 0.065 0.944 0.756 0.769 4.81% r = 0.81
TWO SCREEN VIEWING ON THE RISE
161
Table 2. Mediation Models Predicting Social Interaction Behaviors and Viewing Intention Social interaction behaviors (mediator) Receptive b (SE) Controls Gender (female) Age Education Predictors Motivation: interaction Engagement/information seeking Passing time/enjoyment behaviors: Receptive Expressive Model fit R2 F Sample N Bootstrapped N
- 0.002 (0.098)* - 0.012 (0.005) 0.097 (0.063)
Viewing intention
Expressive b (SE)
b (SE)
- 0.001 (0.106) - 0.019 (0.005)*** 0.127 (0.073)#
0.422 (0.080)*** 0.081 (0.069) 0.084 (0.042)* — — 0.265 22.750*** 453 5,000
0.055 (0.091) 0.579 (0.072)*** 0.164 (0.48)** — —
95% bootstrapped CI
- 0.040 (0.09) - 0.02 (0.01)* - 0.11 (0.07)
[ - 0.212, 0.132] [ - 0.028, - 0.010] [ - 0.239, 0.012]
0.23 - 0.13 - 0.01 0.351 - 0.147
[0.10, 0.353] [ - 0.241, - 0.017] [ - 0.082, 0.065] [0.256, 0.446] [ - 0.245, - 0.050]
0.392 45.68*** 453 5,000
(0.06)*** (0.06)* (0.04) (0.05)*** (0.05)**
0.224 16.449*** 453 5,000
Note: Heteroskedasticity-consistent SEs in parentheses. Confidence intervals were calculated using bootstrapped resampling methods (n = 5,000) at a 0.05 significance level. ***p < 0.001; **p < 0.01; *p < 0.05; #p < 0.10. SE, standard error; CI, confidence interval.
Table 2 also shows that SIB (i.e., receptive and expressive) was significantly correlated with viewing intention. Yet, receptive behavior on SNSs (b = 0.351, SE = 0.05, p < 0.001) was found to be the strongest positive predictor of viewing intention. In contrast, an unexpected negative relationship was found between expressive behavior and viewing intention (b = –0.147, SE = 0.05, p < 0.01), suggesting that receptive and expressive behaviors serve fundamentally different roles in gratifying needs for TSV behaviors. In order to better understand the nature of this mediation, specific direct and indirect effects of motivations were probed through SIB, as presented in Table 3 and Figure 2. Social interaction both directly (b = 0.227, SE = 0.06, p < 0.001) and indirectly influenced future intention to watch entertainment programs. More importantly, however, the indirect effects of interaction motivation on viewing intention was not significant unless mediated through receptive behavior (b = 0.148, SE = 0.03, p < 0.05). In contrast, the effects of engagement with entertainment programs/information seeking on viewing intention were found to be mediated only by expressive behavior
(b = 0.085, SE = 0.03, p < 0.05). The significant path coefficient indicates that H4 is supported. Discussion
A close examination of the results reveals several implications regarding the relationship between motivations for TSV, SIB, and subsequent viewing intention. First, social interaction both directly and indirectly influenced viewers’ intention to watch entertainment programs, although its indirect effect was only significant when mediated through receptive behaviors. In contrast, the indirect effects of engagement with reality show/information seeking were found to be negatively associated with subsequent viewing intention through expressive behaviors. Lastly, passing time was not a significant direct predictor of subsequent viewing intention, but its indirect effects on viewing intention were found to be mediated by both of SIB—more specifically, positive indirect effects through receptive behaviors and negative indirect effects through expressive behaviors. These findings shed light on SNS users’
Table 3. Specific Direct and Indirect Effects of Motivations on Viewing Intention Motivation Social interaction Engagement/information seeking Passing time
Indirect through (mediator) (Direct effect) Receptive behavior Expressive behavior (Direct effect) Receptive behavior Expressive behavior (Direct effect) Receptive behavior Expressive behavior
Effect 0.227 0.148 - 0.008 - 0.129 0.028 - 0.085 - 0.009 0.030 - 0.024
(0.064)*** (0.036)* (0.014) (0.057)* (0.024) (0.031)* (0.037) (0.015)* (0.010)*
Bias-corrected CI [0.100, [0.085, [ - 0.041, [ - 0.241, [ - 0.016, [ - 0.157, [ - 0.082, [0.003, [ - 0.048,
0.353] 0.225] 0.016] - 0.017] 0.080] - 0.032] 0.065] 0.062] - 0.009]
Note: Confidence intervals were calculated using bootstrapped resampling methods (n = 5,000) at a 0.05 significance level. Each motivation was submitted to regression equations separately while controlling for the other two motivations. *p < 0.05; **p < 0.01; ***p < 0.001.
162
FIG. 2.
SHIM ET AL.
Primary results.
behaviors within the context of TV viewing experience, and thus the present study further develops the existing body of research30 focusing on SNS users’ behaviors and interactions. On the one hand, passing time and engagement with reality shows/information seeking31 have long been regarded as functions that take advantage of traditional media.32 This suggests that TSV is pursued with some of the same needs for watching TV identified in prior studies. However, the three motivations for TSV—social interaction, engagement with reality show/information seeking, and passing time—did not appear to be fundamentally new dispositional motivations that are unique to SNSs. Rather, they were new instantiations of existing media use motivations identified in previous research.33 Further, this research suggests that (a) the emergence of new media does not necessarily create fundamentally new motivational needs, and (b) human dispositional motivations, presumably more reflective of enduring characteristics, seem to have more predictive power in one’s media use behavior than situational motivations. The findings of the present study also reveal that audiences who are motivated to gratify their social interaction needs— compared to viewers who seek information, involvement in programs, or want to pass time—tend to be interested in learning about other audiences’ opinions or reactions (e.g., photos, news, etc.). This also supports Haridakis and Hanson’s12 research of co-viewing as a means of social interaction with others. As seen in Tables 2 and 3, receptive behavior was found to be as the most meaningful mediator. Receptive behavior significantly mediated the influence of the social interaction and enjoyment motivations. Overall, the present study suggests that motivational factors were as significant and meaningful antecedents of SIB on SNSs. Furthermore, SIB is significantly and meaningfully associated with future viewing intention. These findings directly extend prior research suggesting that there are links between motivations and SIB regardless of time dimensions (e.g., postexposure behavior).34 Perhaps the most important implication of the results is that TSV emerges as a new practice of providing shared
experiences and a new means of social connectedness by stimulating audiences’ SIB such as posting replies, sharing information and opinion, and even making specific recommendations regarding the contents of entertainment programs. TSV could help build and nourish social capital by binding audiences together under shared or common interests or experiences. Putnam35 also notes that the theory of social capital presumes that the more people connect with other people, the more they trust them, and vice versa. In essence, TSV makes the TV viewing experience multifaceted and stimulating, and thus provides a new way of experiencing entertainment programs on TV. A recent study36 showed that a significant proportion of respondents perceive that TV connected to SNSs is an effective means of communicating with others, which makes watching TV more fun or interesting. Overall, TSV enables viewers to communicate with others and to overcome physical barriers, thus stimulating interactions including information sharing with regard to broadcast programs of interest37—all of which are important factors in building audience loyalty and maintaining audiences or market share. This result comes on the back of the fact that TV is becoming social adhesive again with SNSs. Limitations and future directions
First, this study draws on the implicit assumption that a particular set of motivations drive SIB and future engagement with entertainment programs. However, from a methodological perspective, it is difficult to rule out alternative possibilities such as reciprocal causalities when using crosssectional data such as those used in this study. Indeed, the recent proliferation of the use of SNSs via smartphone platforms may ‘‘cultivate’’ the TSV phenomenon through vicarious modeling, and therefore could promote unique motivations and SIB at a later time. Second, future studies are necessary to investigate social and psychological characteristics predicting the diverse forms or activities of TSV. For instance, locus of control has been positively related to the volume of watching TV,38
TWO SCREEN VIEWING ON THE RISE
postviewing perceptions,39 and sharing of videos on YouTube.12 Innovativeness has also been considered a predictor of a person’s level of Internet use.40 Third, this study has focused on active viewers of entertainment programs who spend more than 5 hours watching entertainment programs. It thus excluded nonactive viewers (i.e., those who spend less than 5 hours watching entertainment programs). The average running time of entertainment programs of the major network broadcasting stations in South Korea is around 40–50 minutes per program.41 These 5 hours of entertainment exposure were considered as sufficient for meaningful TSV-related behavior to be observed for the active viewers. It is based on somewhat subjective criteria. Yet, given that this is a relatively new phenomenon and there is therefore no prior research to guide such sample eligibility, the best and most reasonable assumptions have been made. Nevertheless, such ambiguity is acknowledged in determining sample eligibility, and future research should attempt to employ more theoretically and empirically grounded criteria. As with the first studies that explored the TSV phenomenon, it is our hope that this paper will provide a reasonable and useful starting point to explore such questions. Forth, the findings of the present study suggested that TSV and SIB directly or indirectly affect future viewing intentions of a respective entertainment program. This empirical pattern further implies that TSV behaviors would have meaningful consequences in audience engagements, which has long been a primary concern for media content providers, advertisers, and entertainment producers. Fifth, it would be more fruitful for a future study to attempt to include a more diverse range of subjects (e.g., in terms of age and education attainment) that better reflects general trends in the use of TSV and SNSs among the general population. Previous studies suggest that the use of TSV or SNS via smartphone or other mobile devises has increased substantially among teens and adolescents.42,43 In addition, the amount of entertainment media use or mobile use for the younger population is no less than that of adults in general.43,44 It is plausible to consider the possibility that certain sample-specific characteristics such as age or education levels would have different relationships with TSV behavior and subsequent SIB phenomenon. Finally, this study closes by acknowledging the possibility that the results of the study might have been very different had the focus been on other programs rather than entertainment programs. Future studies should investigate the possibility of genre specific or topic specific TSV motivations and different types of interaction patterns. For example, it is plausible that motivations and their effect on TSV of political news may significantly differ from those driving the viewing of entertainment programs. Audiences of such programs may seek out opinions of others on SNSs while watching such programs in order to justify their preexisting attitude,45 to reduce dissonance,46 or to interact with those whose opinions differ from their own.47 In any case, the core theoretical framework presented in this study is proposed, relying on the interplay among motivations, SIB, and cognitive/behavioral outcomes, as a valuable conceptual tool for examining social viewing behaviors via TSV. Further research in this area is essential to broaden the understanding of the socially bounded nature of media’s effects and their underlying causal mechanisms such as
163
orientations, stimuli, reasoning, orientations, and responses in the cognitive mediation process. Notes
a. The reported average length (around 40–50 minutes per program) of entertainment programs suggests that watching entertainment programs for 5 hours or more per week is equivalent to watching seven programs per week or one program per day on average (40–50 minutes · 7 programs = 4.66– 5.83 hours, then rounded to the nearest integer). Author Disclosure Statement
No competing financial interests exist. References
1. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: homophily in social networks. Annual Review of Sociology 2001; 27:415–444. 2. Bjur J. (2009) Transforming audience. Patterns of individualization in television viewing. Go¨teborg: Department of Journalism and Mass Communication. 3. Brookes M. (2004) Watching alone: social capital and public services broadcasting: a report. Work Foundation in partnership with the BBC. 4. Ferguson D. (2012) The trivial pursuits of mass audiences using social media: a content analysis of Facebook wall posts by fans of top-trending television programs. In Noor AlDeen H, Hendricks JA, eds. Social media: usage and impact. Plymouth, UK: Lexington Books, pp. 39–56. 5. Johns MD. (2012) Two screen viewing and social relationships. Proceedings of the Eighth International Conference on Cultural Attitudes Towards Technology and Communication. Aarhus, Denmark, pp. 333–343. 6. Chen G. Tweet this: a uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Journal of Computers in Human Behavior 2011; 27:755–762. 7. Houston JB, Hawthorne J, Matthew LS, et al. Tweeting during presidential debates: effect on candidate evaluations and debate attitudes. Argumentation & Advocacy 2013; 49:301–311. 8. Fiske J. (1982) Introduction to communication studies. New York: Methuen. 9. Hunt D, Atkin D, Krishnan A. The influence of computermediated communication apprehension on motivations for Facebook use. Journal of Broadcasting & Electronic Media 2012; 56:187–202. 10. Sheldon P. The relationship between unwillingness-tocommunicate and students’ Facebook use. Journal of Media Psychology 2008; 20:67–75. 11. Smock AD, Ellison NB, Lampe C, et al. Facebook as a toolkit: a uses and gratification approach to unbundling feature use. Computers in Human Behavior 2011; 27:2322–2329. 12. Haridakis PM, Hanson G. Audience activity and co-viewing with YouTube: blending mass communication reception and social connection. Journal of Broadcasting & Electronic Media 2009; 53:317–335. 13. Papacharissi Z, Mendelson A. (2011) Toward a new(er) sociability: uses, gratifications, and social capital on Facebook. In Papathanassopoulos S, ed. Media perspectives for the 21st century. London: Routledge, pp. 212–230. 14. Ellison NB, Steinfield C, Lampe C. The benefits of Facebook friends: social capital and college students’ use of
164
15. 16.
17. 18. 19.
20. 21.
22. 23.
24.
25. 26. 27. 28. 29. 30. 31. 32. 33.
SHIM ET AL.
online social network sites. Journal of Computer-Mediated Communication 2007; 12:1143–1168. Levy M, Windahl S. Audience activity and gratifications: a conceptual clarification and exploration. Communication Research 1984; 11:51–78. McPherson K, Huotari K, Cheng F, et al. (2012) Glitter: a mixed-methods study of Twitter use during glee broadcasts. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work Companion. New York: ACM, pp. 167–170. Kim J, Rubin AM. The variable influence of audience activity on media effects. Communication Research 1997; 24:107–135. Rubin AM, Perse EM. Audience activity and television news gratifications. Communication Research 1987; 14:58–84. Doughty M, Rowland D, Lawson S. (2011) Co-viewing live TV with digital backchannel streams. Proceedings of the 9th International Interactive TV & Video Conference (EuroITV ’11). Lisbon, Portugal, pp. 141–144. Perse EM. Audience selectivity and involvement in the newer media environment. Communication Research 1990; 17:675–697. Mitchell K, Jones A, Ishmael J, et al. (2010) TSV: toward content navigation using social awareness. Proceedings of the 8th International Interactive Conference on Interactive TV & Video. Tampere, Finland, pp. 283–292. Hall A. Viewers’ perceptions of reality programs. Communication Quarterly 2006; 54:191–211. Barker V. Older adolescents’ motivations for social network site use: the influence of gender, group identity, and collective self-esteem. CyberPsychology & Behavior 2009; 12: 209–213. Lin K, Lu H. Why people use social networking sites: an empirical study integrating network externalities and motivation theory. Computers in Human Behavior 2011; 27:1152–1161. Leung L, Wei R. More than just talk on the move: uses and gratifications of the cellular phone. Journalism & Mass Communication Quarterly 2000; 77:308–320. Torrez-Riley J. (2011) The TSV phenomenon: new technologies look to enhance television’s role as an enabler of audience activity. Elon, NC: Elon University. Hayes AF. (2013) Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York: Guilford Press. Godlewski LR, Perse EM. Audience activity and reality television: identification, online activity, and satisfaction. Communication Quarterly 2010; 58:148–169. Rubin AM. An examination of television viewing motivations. Communication Research 1981; 8:141–165. Karnik M, Oakley I, Venkatanathan J, et al. Uses and gratifications of a Facebook media sharing group. Proceedings of ACM CSCW 2013, San Antonio, TX. Lasswell HD. (1948) The structure and function of communication in society. In Bryson L, ed. The communication of ideas. New York: Harper, pp. 37–51. Haridakis PM. Audience characteristics, exposure to television violence, and aggression. Media Psychology 2002; 4:323–352. Noble RS. Toward a theory of life-span development. Advances in Psychology 1987; 40:219–230.
34. Rubin AM. Television uses and gratifications: the interactions of viewing patterns and motivations. Journal of Broadcasting & Electronic Media 1983; 27:37–51. 35. Putnam RD. Tuning in, tuning out: the strange disappearance of social capital in America. Political Science & Politics 1995; 28:664–683. 36. Casciato P. (2011) New TV and Social media trend among the youth-study. www.reuters.com/article/2011/03/08/ us-digital-social-tv-idUSTRE7275VC20110308 (accessed Jun. 11, 2014). 37. Hutchison J. (2011) Brand and marketing research round-up. www.the-cma.com/news/brand-and-marketing-research-roundup (accessed Jun. 16, 2014). 38. Wober JM, Gunter B. Television audience research at Britain’s independent broadcasting authority, 1974–1984. Journal of Broadcasting & Electronic Media1986; 30:15–31. 39. Wober JM, Gunter B. Television and personal threat: fact or artifact? A British survey. British Journal of Social Psychology1982; 21:239–247. 40. Busselle R, Reagan J, Pinkleton B, et al. Factors affecting Internet use in a saturated-access population. Telematics & Informatics1999; 16:45–58. 41. Jung YC, Kim YW. (2013) Broadcast media behavior report in South Korea—2013. Korea Communications Commission. www.kisdi.re.kr/kisdi/jsp/fp/kr/search/search.jsp (accessed Dec. 4, 2014). 42. Duggan M, Brenner J. (2013) The demographics of social media users—2012. Pew Internet & American Life Project. www.pewinternet.org/2013/02/14/the-demographics-ofsocial-media-users-2012/ (accessed Dec. 4, 2014). 43. Lenhart A, Purcell K, Smith A, et al. (2010) Social media and mobile Internet use among teens and young adults. Pew Internet & American Life Project. www.pewinternet.org/ 2010/02/03/social-media-and-young-adults (accessed Dec. 4, 2014). 44. Mark AE, Boyce WF, Janssen I. Television viewing, computer use and total screen time in Canadian youth. Paediatrics & Child Health 2006; 11:595–599. 45. Taber CS, Lodge M. Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science 2006; 50:755–769. 46. Himelboim I, McCreery S, Smith M. Birds of a feather tweet together: integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-Mediated Communication 2013; 18:40–60. 47. Price V. (2009) Citizens deliberating online: theory and some evidence. In Davies T, Noveck S, eds. Online deliberation: design, research, and practice. Chicago: University of Chicago Press, pp. 37–58.
Address correspondence to: Dr. Hongjin Shim Broadcasting Media Research Division Korea Information Society Development Institute 18 Jeongtong-ro Deoksan-myeon Jincheon-gun Chungcheongbuk-do, 365-841 Korea E-mail: [email protected]
Copyright of CyberPsychology, Behavior & Social Networking is the property of Mary Ann Liebert, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
An Exploration of Motivations for Two Screen Viewing, Social Interaction Behaviors, and Factors that Influence Viewing Intentions Hongjin Shim, PhD,1 Poong Oh,2 Hyunjin Song,3 and Yeonkyung Lee 4
Abstract
This study explores whether, and how, motivations for two screen viewing predicted social interaction behaviors and subsequent viewing intention of TV programs. A total of 453 respondents who responded that they use social networking sites (SNSs) via smartphones and actively watch entertainment programs completed an online survey questionnaire. In agreement with uses and gratifications assumptions, motivations for TSV predicted distinctive sets of social interaction behaviors, which mediated the influence of motivations on viewing intentions. Respondents’ two screen viewing was meaningfully related with social interaction, engagement with programs, information seeking, and passing time. Results suggest that two screen viewing could provide shared experiences nourishing social capital and reintegrate TV audiences by social adhesive resulting from TV with SNSs.
Introduction
S
ocial and communication relations are likely to develop between those who share common interests and experiences.1 Since TV became available as a primary medium to the public, it has served as an important source of common interests and experience, functioning as ‘‘social adhesive’’2 and ‘‘water coolers.’’3 However, the drastic change in the media environment from the 1980s, in particular the increase in the number of channels, led to audience fragmentation. People tend to expose themselves selectively only to what they want to watch and thus can hardly share their interests and experiences with those who have different preferences and tastes. As a result, people are unlikely to develop common interests and experiences by watching TV in a multichannel environment. Recently, however, the social function of TV as a source of common interests and experiences has gained renewed attention, as social networking sites (SNSs) emerge as alternative communication channels, specifically among young adults. SNS users freely exchange their opinions of TV programs that they watch, which enhances the social interactions among SNS users who watch the same TV programs. This implies that SNSs can reintegrate TV viewers, allowing them to share their interests and experiences.4
It is worth noting that social interactions among TV viewers often occur through SNSs. According to Johns,5 people often read and leave messages about TV programs on SNSs using secondary devices, such as mobile phones and tablet PC. In other words, TV viewers look at two or more screens at the same time: one is a TV screen used to watch the TV program, and the other is a secondary device used as the backchannel for social interaction through SNSs. This new TV viewing behavior is called ‘‘two screen viewing’’ behavior (TSV).5 The primary goals of this study are twofold: (a) to explore the reasons why viewers are participating in these social interaction behaviors, and (b) to investigate the effects of such social interaction behaviors on TSV. Whereas many studies have been conducted on SNSs focusing on social interactions taking place on them,6,7 little scholarly attention has been paid to the potential antecedents and effects of TSV. Further, it has not been answered to what extent TSV promotes people to share common interest or experiences, thereby reintegrating TV viewers and empowering people to generate meanings from TV programs that are not intended by the producers.8 From this perspective, the present study investigates the antecedents and implications of TSV by exploring (a) motivations for TSV, (b) social interaction behaviors (SIB) that TSV leads to, and (c) their relationships with TV viewing intentions as one of the effects of TSV.
1
Broadcasting Media Research Division, Korea Information Society Development Institute, Jincheon-gun, Korea. Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, California. School of Communication, The Ohio State University, Columbus, Ohio. 4 School of Communication, Yonsei University, Seoul, Korea. 2 3
158
TWO SCREEN VIEWING ON THE RISE
SNSs are a key component of TSV functioning as a space for people to share their thoughts or opinions as well as common interest on TV programs. To understand motivations for TSV, it is necessary first to recognize motivations for SNSs. Previous research has identified several motivations for SNSs, which include self-expression, entertainment, passing time,9 relationship maintenance,10 and social interaction.11 More recently, co-viewing has been pointed out as an additional motivation for the use of YouTube.12 The motivations of entertainment and passing time10 have generally been recognized as major motivations for SNSs use as well. Haridakis and Hanson12 maintained that social interaction is another important motivation for the use of SNSs, through which people with common interests are connected.13,14 Therefore, based on the above literature on the motivations for media use, the following hypothesis is presented: H1: The main motivations for TSV will be social interaction, information seeking, and passing time.
Previous studies suggest that media use motivations are closely related to media selection.12, 15 Within the context of SNS use, different motivations result in various communication behaviors, for instance status updates on Facebook and real time chat.9 In the same way, motivations for TSV could affect behavior related to SIB. A study by McPherson et al.16 suggests that motivations for watching TV programs are significantly associated with sharing opinions about TV programs on Twitter. It was expected that a substantial relationship between motivations for TSV and SIB would exist. SIB is multidimensional,15 as people are variably active along several dimensions in the media use process.17,18 Thus, this study takes a different approach to SIB, in that two types of SIB were differentiated: receptive or expressive behaviors. In this context, this study proposes the following hypothesis: H2: The motivations of social interaction, information seeking, and passing time for TSV will be associated with receptive or expressive behaviors on SNSs.
An increasing number of mobile phone users use their phones to communicate with others while watching TV5 via SNSs that are readily available via smartphones. The SIB in SNSs among users serves as a backchannel, in which people can express both their opinions and emotions,19 or exchange the information about specific TV programs. Talking and obtaining information about particular TV programs satisfies people’s needs to share, seek, or even enjoy their thoughts, emotions, or opinions. Further, many studies have long supported that watching TV increases intention to watch specific program content, attention to program content, and cognitive and affective involvement with programs.8,15,20 These viewing behaviors are likely to boost intention to view or exposure to TV content mentioned by others at a later time.21 Given the motivations are one of the significant predictors of future TV viewing intention, SIB can also become a vehicle leading to intention to view. Thus, this study proposes the following hypotheses: H3: Intention to view can be predicted by the motivations of social interaction, information seeking, and passing time for TSV. H4: Receptive or expressive behaviors will mediate the relationship between TSV users’ motivations and intention to view.
159 Methods Data
The present study analyzed self-reported survey data collected from a random sample of 453 respondents who reported that they use SNSs via smartphones and actively watch entertainment programs for 5 hours or more per week.a The focus was on entertainment programs in particular because such programs enable viewers to experience ‘‘real’’ worlds vicariously through observations of others’ trials and tribulations,22 driving social viewing behaviors. The survey was conducted between March 10 and 18, 2013, in the metropolitan Seoul area of South Korea, and was administered online. Measures Motivations for TSV use. A scale tapping respondents’ motivations for using TSV was constructed following previous studies.23–25 Respondents were asked how likely they were to engage in TSV use on16 items (e.g., ‘‘to talk with others to share feelings on TV programs’’), each of which were measured on a 7-point scale (‘‘not at all likely’’ to ‘‘extremely likely’’). Preliminary analysis using exploratory factor analysis (principle component analysis using promax rotation with Kaiser normalization) revealed three motivations that drive TSV use. Social interaction behaviors on SNSs. The Interactional Behaviors Scale9,26 was adapted to gauge audience activities as SIB. The respondents were asked to answer 16 questions pertaining to how often they engaged in specific SIB on SNSs, using a 7-point scale (‘‘not at all’’ to ‘‘very often’’). Expressive behaviors were measured using 11 items, including ‘‘How frequently do you express emotions about TV programs on SNSs?’’ (Cronbach’s a = 0.95, M = 3.94, SD = 1.42). Receptive behaviors were measured with five items, including ‘‘How often do you watch streams of comments posted by others?’’ (Cronbach’s a = 0.88, M = 4.70, SD = 1.20). Viewing intentions. The respondents were asked three questions about the likelihood of watching entertainment programs based on a 7-point scale (‘‘not at all’’ to ‘‘extremely likely’’). Using the responses, a summary scale of viewing intention toward entertainment programs (three items; Cronbach’s a = 0.95, M = 5.6, SD = 1.07) was constructed. Demographic controls. All analyses included three demographic variables as control variables. The majority were female (51.1%). Age ranged from 21 to 64 years (M = 36.08 years, SD = 10.49 years). The sample tended to be highly educated; 82.3% of total respondents had a college degree. Analysis strategy
Exploratory factor analyses (using principal components analysis with promax rotation) were performed for the first H1. In testing simple mediation effects (H2–H4, as shown in Figure 1), the PROCESS macro27 was used to estimate a series of bootstrapped resampling-based regression models, which yields more valid and statistically powerful results than its alternatives.27 Across the analyses, heteroskedasticity-consistent standard errors and bias-corrected maximum likelihood
160
SHIM ET AL.
FIG. 1.
Hypothesized model.
confidence intervals were estimated using a total of 5,000 bootstrapping-based resamples, controlling for covariates for both the focal mediators and the dependent variable, entertainment program viewing intention. Results Motivations for TSV
Table 1 summarizes the results of factor analysis. Factor 1, social interaction, was comprised of eight items relevant to sharing an activity, such as expressing emotions about the program, sharing opinions about the program, or communicating with others (Cronbach’s a = 0.91, M = 4.72, SD = 1.08, explained variance = 47.51%). Of the five items for factor 2, engagement with entertainment programs/information seeking loaded most strongly on this factor. This related to involvement in reality shows and information gained from programs. In addition, items tapping the extent to which the respondents’ perceived an ability to influence programming by commenting on a program also loaded highly on this factor (Cronbach’s a = 0.86, M = 4.31, SD = 1.21, explained variance = 7.70%). Of the two items for factor 3, passing time was comprised of items tapping TSV as a tool to pass the time by watching TV (Pearson’s r = 0.81, M = 4.18, SD = 1.39, explained variance = 4.81%). The three motivation factors accounted for 60.00% of the total variance. Among the three motivation factors that were identified, social interaction had the highest mean scores, whereas en-
gagement with entertainment programs/information seeking and passing time were slightly less salient for TSV use. The findings suggest that respondents engage in TSV primarily to share their emotions or opinions, rather than to obtain information about the program or to seek enjoyment. Except for engagement with entertainment programs, motivations for TSV use in this study were partly identified in prior studies: passing time, entertainment, habit, companionship, information learning, escape, self-expression, identification, and social interaction.9,28,29 Therefore, H1 is partially supported. Relationships among motivations, SIB, and viewing intention
The first two columns in Table 2 show that all three of the motivations were found to be significant predictors of respondents’ actual SIB, receptive and expressive behaviors. Social interaction positively predicted receptive behavior (b = 0.422, SE = 0.08, p < 0.01), but not expressive behavior (b = 0.055, SE = 0.09, p > 0.05) above and beyond the effect of other control variables. In contrast, the opposite pattern was found for engagement with entertainment programs/ information seeking in that the motivation did not predict receptive behavior (b = 0.081, SE = 0.06, p > 0.05) but significantly correlated with expressive behavior (b = 0.579, SE = 0.07, p < 0.001). Lastly, passing time was significantly correlated with both receptive behavior (b = 0.084, SE = 0.04, p < 0.05) and expressive behavior (b = 0.164, SE = 0.48, p < 0.01) examined in this study, although the magnitude of the coefficient for passing time was somewhat smaller than that of social interaction, engagement with the program, or information seeking. H2 is therefore also supported. The third and fourth columns of Table 3 show the results of the simple mediation analysis, which suggest that the direct effects of social interaction (b = 0.227, SE = 0.06, p < 0.001) and engagement with entertainment programs/ information seeking (b = –0.129, SE = 0.05, p < 0.05) were significant predictors of intention to watch entertainment programs, supporting H3.
Table 1. Primary Factor Loadings of Motivations for Using Two Screen Viewing
To watch TV together To share my opinion on TV programs with others To find out something related to TV programs To chat away with friends while watching TV To have fun To find out others’ views and thoughts To interact with others To socialize with others To share a sense of realism and suspense with others To help keep programs I like on the air for many years to come To put across my views or opinions to producers of programs To boost the TV ratings of my favorite program To get a lot of information about programs I like To acquire new information about various programs Feel bored To pass the time by watching TV Eigenvalue % Variance explained Cronbach’s a
Factor 1
Factor 2
Factor 3
0.526 0.858 0.743 0.692 0.668 0.639 0.835 0.641 0.533 0.281 - 0.118 - 0.071 0.231 0.122 0.044 - 0.063 7.601 47.51% 0.91
0.292 0.024 0.100 - 0.091 - 0.158 0.144 - 0.037 0.062 0.259 0.564 0.797 0.787 0.544 0.686 - 0.056 0.183 1.231 7.70% 0.86
0.020 - 0.110 - 0.098 0.156 0.282 - 0.134 0.010 0.099 0.017 - 0.025 0.056 0.132 - 0.013 - 0.065 0.944 0.756 0.769 4.81% r = 0.81
TWO SCREEN VIEWING ON THE RISE
161
Table 2. Mediation Models Predicting Social Interaction Behaviors and Viewing Intention Social interaction behaviors (mediator) Receptive b (SE) Controls Gender (female) Age Education Predictors Motivation: interaction Engagement/information seeking Passing time/enjoyment behaviors: Receptive Expressive Model fit R2 F Sample N Bootstrapped N
- 0.002 (0.098)* - 0.012 (0.005) 0.097 (0.063)
Viewing intention
Expressive b (SE)
b (SE)
- 0.001 (0.106) - 0.019 (0.005)*** 0.127 (0.073)#
0.422 (0.080)*** 0.081 (0.069) 0.084 (0.042)* — — 0.265 22.750*** 453 5,000
0.055 (0.091) 0.579 (0.072)*** 0.164 (0.48)** — —
95% bootstrapped CI
- 0.040 (0.09) - 0.02 (0.01)* - 0.11 (0.07)
[ - 0.212, 0.132] [ - 0.028, - 0.010] [ - 0.239, 0.012]
0.23 - 0.13 - 0.01 0.351 - 0.147
[0.10, 0.353] [ - 0.241, - 0.017] [ - 0.082, 0.065] [0.256, 0.446] [ - 0.245, - 0.050]
0.392 45.68*** 453 5,000
(0.06)*** (0.06)* (0.04) (0.05)*** (0.05)**
0.224 16.449*** 453 5,000
Note: Heteroskedasticity-consistent SEs in parentheses. Confidence intervals were calculated using bootstrapped resampling methods (n = 5,000) at a 0.05 significance level. ***p < 0.001; **p < 0.01; *p < 0.05; #p < 0.10. SE, standard error; CI, confidence interval.
Table 2 also shows that SIB (i.e., receptive and expressive) was significantly correlated with viewing intention. Yet, receptive behavior on SNSs (b = 0.351, SE = 0.05, p < 0.001) was found to be the strongest positive predictor of viewing intention. In contrast, an unexpected negative relationship was found between expressive behavior and viewing intention (b = –0.147, SE = 0.05, p < 0.01), suggesting that receptive and expressive behaviors serve fundamentally different roles in gratifying needs for TSV behaviors. In order to better understand the nature of this mediation, specific direct and indirect effects of motivations were probed through SIB, as presented in Table 3 and Figure 2. Social interaction both directly (b = 0.227, SE = 0.06, p < 0.001) and indirectly influenced future intention to watch entertainment programs. More importantly, however, the indirect effects of interaction motivation on viewing intention was not significant unless mediated through receptive behavior (b = 0.148, SE = 0.03, p < 0.05). In contrast, the effects of engagement with entertainment programs/information seeking on viewing intention were found to be mediated only by expressive behavior
(b = 0.085, SE = 0.03, p < 0.05). The significant path coefficient indicates that H4 is supported. Discussion
A close examination of the results reveals several implications regarding the relationship between motivations for TSV, SIB, and subsequent viewing intention. First, social interaction both directly and indirectly influenced viewers’ intention to watch entertainment programs, although its indirect effect was only significant when mediated through receptive behaviors. In contrast, the indirect effects of engagement with reality show/information seeking were found to be negatively associated with subsequent viewing intention through expressive behaviors. Lastly, passing time was not a significant direct predictor of subsequent viewing intention, but its indirect effects on viewing intention were found to be mediated by both of SIB—more specifically, positive indirect effects through receptive behaviors and negative indirect effects through expressive behaviors. These findings shed light on SNS users’
Table 3. Specific Direct and Indirect Effects of Motivations on Viewing Intention Motivation Social interaction Engagement/information seeking Passing time
Indirect through (mediator) (Direct effect) Receptive behavior Expressive behavior (Direct effect) Receptive behavior Expressive behavior (Direct effect) Receptive behavior Expressive behavior
Effect 0.227 0.148 - 0.008 - 0.129 0.028 - 0.085 - 0.009 0.030 - 0.024
(0.064)*** (0.036)* (0.014) (0.057)* (0.024) (0.031)* (0.037) (0.015)* (0.010)*
Bias-corrected CI [0.100, [0.085, [ - 0.041, [ - 0.241, [ - 0.016, [ - 0.157, [ - 0.082, [0.003, [ - 0.048,
0.353] 0.225] 0.016] - 0.017] 0.080] - 0.032] 0.065] 0.062] - 0.009]
Note: Confidence intervals were calculated using bootstrapped resampling methods (n = 5,000) at a 0.05 significance level. Each motivation was submitted to regression equations separately while controlling for the other two motivations. *p < 0.05; **p < 0.01; ***p < 0.001.
162
FIG. 2.
SHIM ET AL.
Primary results.
behaviors within the context of TV viewing experience, and thus the present study further develops the existing body of research30 focusing on SNS users’ behaviors and interactions. On the one hand, passing time and engagement with reality shows/information seeking31 have long been regarded as functions that take advantage of traditional media.32 This suggests that TSV is pursued with some of the same needs for watching TV identified in prior studies. However, the three motivations for TSV—social interaction, engagement with reality show/information seeking, and passing time—did not appear to be fundamentally new dispositional motivations that are unique to SNSs. Rather, they were new instantiations of existing media use motivations identified in previous research.33 Further, this research suggests that (a) the emergence of new media does not necessarily create fundamentally new motivational needs, and (b) human dispositional motivations, presumably more reflective of enduring characteristics, seem to have more predictive power in one’s media use behavior than situational motivations. The findings of the present study also reveal that audiences who are motivated to gratify their social interaction needs— compared to viewers who seek information, involvement in programs, or want to pass time—tend to be interested in learning about other audiences’ opinions or reactions (e.g., photos, news, etc.). This also supports Haridakis and Hanson’s12 research of co-viewing as a means of social interaction with others. As seen in Tables 2 and 3, receptive behavior was found to be as the most meaningful mediator. Receptive behavior significantly mediated the influence of the social interaction and enjoyment motivations. Overall, the present study suggests that motivational factors were as significant and meaningful antecedents of SIB on SNSs. Furthermore, SIB is significantly and meaningfully associated with future viewing intention. These findings directly extend prior research suggesting that there are links between motivations and SIB regardless of time dimensions (e.g., postexposure behavior).34 Perhaps the most important implication of the results is that TSV emerges as a new practice of providing shared
experiences and a new means of social connectedness by stimulating audiences’ SIB such as posting replies, sharing information and opinion, and even making specific recommendations regarding the contents of entertainment programs. TSV could help build and nourish social capital by binding audiences together under shared or common interests or experiences. Putnam35 also notes that the theory of social capital presumes that the more people connect with other people, the more they trust them, and vice versa. In essence, TSV makes the TV viewing experience multifaceted and stimulating, and thus provides a new way of experiencing entertainment programs on TV. A recent study36 showed that a significant proportion of respondents perceive that TV connected to SNSs is an effective means of communicating with others, which makes watching TV more fun or interesting. Overall, TSV enables viewers to communicate with others and to overcome physical barriers, thus stimulating interactions including information sharing with regard to broadcast programs of interest37—all of which are important factors in building audience loyalty and maintaining audiences or market share. This result comes on the back of the fact that TV is becoming social adhesive again with SNSs. Limitations and future directions
First, this study draws on the implicit assumption that a particular set of motivations drive SIB and future engagement with entertainment programs. However, from a methodological perspective, it is difficult to rule out alternative possibilities such as reciprocal causalities when using crosssectional data such as those used in this study. Indeed, the recent proliferation of the use of SNSs via smartphone platforms may ‘‘cultivate’’ the TSV phenomenon through vicarious modeling, and therefore could promote unique motivations and SIB at a later time. Second, future studies are necessary to investigate social and psychological characteristics predicting the diverse forms or activities of TSV. For instance, locus of control has been positively related to the volume of watching TV,38
TWO SCREEN VIEWING ON THE RISE
postviewing perceptions,39 and sharing of videos on YouTube.12 Innovativeness has also been considered a predictor of a person’s level of Internet use.40 Third, this study has focused on active viewers of entertainment programs who spend more than 5 hours watching entertainment programs. It thus excluded nonactive viewers (i.e., those who spend less than 5 hours watching entertainment programs). The average running time of entertainment programs of the major network broadcasting stations in South Korea is around 40–50 minutes per program.41 These 5 hours of entertainment exposure were considered as sufficient for meaningful TSV-related behavior to be observed for the active viewers. It is based on somewhat subjective criteria. Yet, given that this is a relatively new phenomenon and there is therefore no prior research to guide such sample eligibility, the best and most reasonable assumptions have been made. Nevertheless, such ambiguity is acknowledged in determining sample eligibility, and future research should attempt to employ more theoretically and empirically grounded criteria. As with the first studies that explored the TSV phenomenon, it is our hope that this paper will provide a reasonable and useful starting point to explore such questions. Forth, the findings of the present study suggested that TSV and SIB directly or indirectly affect future viewing intentions of a respective entertainment program. This empirical pattern further implies that TSV behaviors would have meaningful consequences in audience engagements, which has long been a primary concern for media content providers, advertisers, and entertainment producers. Fifth, it would be more fruitful for a future study to attempt to include a more diverse range of subjects (e.g., in terms of age and education attainment) that better reflects general trends in the use of TSV and SNSs among the general population. Previous studies suggest that the use of TSV or SNS via smartphone or other mobile devises has increased substantially among teens and adolescents.42,43 In addition, the amount of entertainment media use or mobile use for the younger population is no less than that of adults in general.43,44 It is plausible to consider the possibility that certain sample-specific characteristics such as age or education levels would have different relationships with TSV behavior and subsequent SIB phenomenon. Finally, this study closes by acknowledging the possibility that the results of the study might have been very different had the focus been on other programs rather than entertainment programs. Future studies should investigate the possibility of genre specific or topic specific TSV motivations and different types of interaction patterns. For example, it is plausible that motivations and their effect on TSV of political news may significantly differ from those driving the viewing of entertainment programs. Audiences of such programs may seek out opinions of others on SNSs while watching such programs in order to justify their preexisting attitude,45 to reduce dissonance,46 or to interact with those whose opinions differ from their own.47 In any case, the core theoretical framework presented in this study is proposed, relying on the interplay among motivations, SIB, and cognitive/behavioral outcomes, as a valuable conceptual tool for examining social viewing behaviors via TSV. Further research in this area is essential to broaden the understanding of the socially bounded nature of media’s effects and their underlying causal mechanisms such as
163
orientations, stimuli, reasoning, orientations, and responses in the cognitive mediation process. Notes
a. The reported average length (around 40–50 minutes per program) of entertainment programs suggests that watching entertainment programs for 5 hours or more per week is equivalent to watching seven programs per week or one program per day on average (40–50 minutes · 7 programs = 4.66– 5.83 hours, then rounded to the nearest integer). Author Disclosure Statement
No competing financial interests exist. References
1. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: homophily in social networks. Annual Review of Sociology 2001; 27:415–444. 2. Bjur J. (2009) Transforming audience. Patterns of individualization in television viewing. Go¨teborg: Department of Journalism and Mass Communication. 3. Brookes M. (2004) Watching alone: social capital and public services broadcasting: a report. Work Foundation in partnership with the BBC. 4. Ferguson D. (2012) The trivial pursuits of mass audiences using social media: a content analysis of Facebook wall posts by fans of top-trending television programs. In Noor AlDeen H, Hendricks JA, eds. Social media: usage and impact. Plymouth, UK: Lexington Books, pp. 39–56. 5. Johns MD. (2012) Two screen viewing and social relationships. Proceedings of the Eighth International Conference on Cultural Attitudes Towards Technology and Communication. Aarhus, Denmark, pp. 333–343. 6. Chen G. Tweet this: a uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Journal of Computers in Human Behavior 2011; 27:755–762. 7. Houston JB, Hawthorne J, Matthew LS, et al. Tweeting during presidential debates: effect on candidate evaluations and debate attitudes. Argumentation & Advocacy 2013; 49:301–311. 8. Fiske J. (1982) Introduction to communication studies. New York: Methuen. 9. Hunt D, Atkin D, Krishnan A. The influence of computermediated communication apprehension on motivations for Facebook use. Journal of Broadcasting & Electronic Media 2012; 56:187–202. 10. Sheldon P. The relationship between unwillingness-tocommunicate and students’ Facebook use. Journal of Media Psychology 2008; 20:67–75. 11. Smock AD, Ellison NB, Lampe C, et al. Facebook as a toolkit: a uses and gratification approach to unbundling feature use. Computers in Human Behavior 2011; 27:2322–2329. 12. Haridakis PM, Hanson G. Audience activity and co-viewing with YouTube: blending mass communication reception and social connection. Journal of Broadcasting & Electronic Media 2009; 53:317–335. 13. Papacharissi Z, Mendelson A. (2011) Toward a new(er) sociability: uses, gratifications, and social capital on Facebook. In Papathanassopoulos S, ed. Media perspectives for the 21st century. London: Routledge, pp. 212–230. 14. Ellison NB, Steinfield C, Lampe C. The benefits of Facebook friends: social capital and college students’ use of
164
15. 16.
17. 18. 19.
20. 21.
22. 23.
24.
25. 26. 27. 28. 29. 30. 31. 32. 33.
SHIM ET AL.
online social network sites. Journal of Computer-Mediated Communication 2007; 12:1143–1168. Levy M, Windahl S. Audience activity and gratifications: a conceptual clarification and exploration. Communication Research 1984; 11:51–78. McPherson K, Huotari K, Cheng F, et al. (2012) Glitter: a mixed-methods study of Twitter use during glee broadcasts. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work Companion. New York: ACM, pp. 167–170. Kim J, Rubin AM. The variable influence of audience activity on media effects. Communication Research 1997; 24:107–135. Rubin AM, Perse EM. Audience activity and television news gratifications. Communication Research 1987; 14:58–84. Doughty M, Rowland D, Lawson S. (2011) Co-viewing live TV with digital backchannel streams. Proceedings of the 9th International Interactive TV & Video Conference (EuroITV ’11). Lisbon, Portugal, pp. 141–144. Perse EM. Audience selectivity and involvement in the newer media environment. Communication Research 1990; 17:675–697. Mitchell K, Jones A, Ishmael J, et al. (2010) TSV: toward content navigation using social awareness. Proceedings of the 8th International Interactive Conference on Interactive TV & Video. Tampere, Finland, pp. 283–292. Hall A. Viewers’ perceptions of reality programs. Communication Quarterly 2006; 54:191–211. Barker V. Older adolescents’ motivations for social network site use: the influence of gender, group identity, and collective self-esteem. CyberPsychology & Behavior 2009; 12: 209–213. Lin K, Lu H. Why people use social networking sites: an empirical study integrating network externalities and motivation theory. Computers in Human Behavior 2011; 27:1152–1161. Leung L, Wei R. More than just talk on the move: uses and gratifications of the cellular phone. Journalism & Mass Communication Quarterly 2000; 77:308–320. Torrez-Riley J. (2011) The TSV phenomenon: new technologies look to enhance television’s role as an enabler of audience activity. Elon, NC: Elon University. Hayes AF. (2013) Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York: Guilford Press. Godlewski LR, Perse EM. Audience activity and reality television: identification, online activity, and satisfaction. Communication Quarterly 2010; 58:148–169. Rubin AM. An examination of television viewing motivations. Communication Research 1981; 8:141–165. Karnik M, Oakley I, Venkatanathan J, et al. Uses and gratifications of a Facebook media sharing group. Proceedings of ACM CSCW 2013, San Antonio, TX. Lasswell HD. (1948) The structure and function of communication in society. In Bryson L, ed. The communication of ideas. New York: Harper, pp. 37–51. Haridakis PM. Audience characteristics, exposure to television violence, and aggression. Media Psychology 2002; 4:323–352. Noble RS. Toward a theory of life-span development. Advances in Psychology 1987; 40:219–230.
34. Rubin AM. Television uses and gratifications: the interactions of viewing patterns and motivations. Journal of Broadcasting & Electronic Media 1983; 27:37–51. 35. Putnam RD. Tuning in, tuning out: the strange disappearance of social capital in America. Political Science & Politics 1995; 28:664–683. 36. Casciato P. (2011) New TV and Social media trend among the youth-study. www.reuters.com/article/2011/03/08/ us-digital-social-tv-idUSTRE7275VC20110308 (accessed Jun. 11, 2014). 37. Hutchison J. (2011) Brand and marketing research round-up. www.the-cma.com/news/brand-and-marketing-research-roundup (accessed Jun. 16, 2014). 38. Wober JM, Gunter B. Television audience research at Britain’s independent broadcasting authority, 1974–1984. Journal of Broadcasting & Electronic Media1986; 30:15–31. 39. Wober JM, Gunter B. Television and personal threat: fact or artifact? A British survey. British Journal of Social Psychology1982; 21:239–247. 40. Busselle R, Reagan J, Pinkleton B, et al. Factors affecting Internet use in a saturated-access population. Telematics & Informatics1999; 16:45–58. 41. Jung YC, Kim YW. (2013) Broadcast media behavior report in South Korea—2013. Korea Communications Commission. www.kisdi.re.kr/kisdi/jsp/fp/kr/search/search.jsp (accessed Dec. 4, 2014). 42. Duggan M, Brenner J. (2013) The demographics of social media users—2012. Pew Internet & American Life Project. www.pewinternet.org/2013/02/14/the-demographics-ofsocial-media-users-2012/ (accessed Dec. 4, 2014). 43. Lenhart A, Purcell K, Smith A, et al. (2010) Social media and mobile Internet use among teens and young adults. Pew Internet & American Life Project. www.pewinternet.org/ 2010/02/03/social-media-and-young-adults (accessed Dec. 4, 2014). 44. Mark AE, Boyce WF, Janssen I. Television viewing, computer use and total screen time in Canadian youth. Paediatrics & Child Health 2006; 11:595–599. 45. Taber CS, Lodge M. Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science 2006; 50:755–769. 46. Himelboim I, McCreery S, Smith M. Birds of a feather tweet together: integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-Mediated Communication 2013; 18:40–60. 47. Price V. (2009) Citizens deliberating online: theory and some evidence. In Davies T, Noveck S, eds. Online deliberation: design, research, and practice. Chicago: University of Chicago Press, pp. 37–58.
Address correspondence to: Dr. Hongjin Shim Broadcasting Media Research Division Korea Information Society Development Institute 18 Jeongtong-ro Deoksan-myeon Jincheon-gun Chungcheongbuk-do, 365-841 Korea E-mail: [email protected]
Copyright of CyberPsychology, Behavior & Social Networking is the property of Mary Ann Liebert, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
