- Email: [email protected]
What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions
Accepted Manuscript What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions
Hongjin Shim, Euikyung Shin, Sohye Lim PII:
S0747-5632(17)30331-X
DOI:
10.1016/j.chb.2017.05.019
Reference:
CHB 4981
To appear in:
Computers in Human Behavior
Received Date:
13 June 2016
Revised Date:
10 May 2017
Accepted Date:
11 May 2017
Please cite this article as: Hongjin Shim, Euikyung Shin, Sohye Lim, What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions, Computers in Human Behavior (2017), doi: 10.1016/j.chb.2017.05.019
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Three primary TSV motivations were identified: social co-viewing, engagement, and passing time. Social interactions were categorized into social sharing and issue surveillance.. Engagement and passing time were associated with social sharing. Innovativeness and BAS had significant impacts on TSV users’ motivations.
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What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions
Hongjin Shim, Ph.D. Research Fellow, Broadcasting Media Research Division Korea Information Society Development Institute 18 Jeongtong-ro, Deoksan-myeon, Jincheon-gun, Chungcheongbuk-do, 365-841, South Korea 82-02-570-4260 [email protected] Euikyung Shin, Ph.D. Candidate Graduate School of Communication and Arts Yonsei University #208 Billingsley Hall, 50 Yonsei-ro, Seodaemun-Gu, Seoul, South Korea 82-10-2956-1809 [email protected] Sohye Lim, Ph.D. (corresponding author) Associate Professor, School of Communication and Media Ewha Womans University Seodaemun-Gu Daehyundong 11-1, Seoul, South Korea 82-10-8669-8901 [email protected]
Manuscript resubmitted to Computers in Human Behavior for a review
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What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions
1. Introduction The experience of TV viewing has long been regarded as one-way communication in which viewers passively receive what the media broadcasts (Thompson, 1995; Torrez-Riley, 2011). The influx of SNSs and mobile technology provides TV viewers with unprecedented opportunities for communication with others in a variety of ways (Hill & Benton, 2012; Neate, Jones, & Evans, 2017). Specifically, viewers simultaneously use multiple screens: the fixed TV screens on which they view the TV series that lead them to social interactions such as sharing opinions or recommending programs and the screens and devices (e.g., smart phones) that viewers use as backchannels to interact with others remotely on SNSs (Kroon, 2017; Shim et al, 2015). This new TV viewing behavior, which embodies one aspect of the phenomenon known as hybrid media, is called ‘‘two-screen viewing’’ or “second screening” (Choi & Jung, 2016; Johns, 2012). This two-screen viewing (TSV) phenomenon is becoming increasingly prevalent; according to a recent report, 21% of tablet users and 30% of smart phone users in the United States use their smart devices while watching TV multiple times a day (Nielson, 2017). Similarly, DMC Media (2017) found in its survey conducted in Korea that 92.8% of respondents had used second screens while watching TV. Despite the prominence of two-screen viewing, key questions remain regarding the antecedents and effects of TSV including: What motivations drive people to participate in TSV? Which user traits influence those motivations? How are those motivations related to actual TSV patterns? Addressing these questions is important for 1
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improving not only our understanding of how people consume TV is evolving but also the way TV viewers are actively interweaving social media into their traditional media use. To investigate these questions, we adopted the uses and gratification approach (U&G), which provides a useful theoretical framework to explore the motivations for TSV. Previous research based on the U&G frame primarily investigated the role of viewers’ needs and expectations in mass communication (e.g., Papacharissi & Mendelson, 2011; Rubin, 1983). For example, numerous studies have shown relaxation, habit, learning, escape, information-seeking, arousal, and entertainment to be the salient gratifications derived from watching TV (Greenberg, 1974; Rubin, 1981; 1983). In addition, studies on the social and psychological origins of needs (e.g., personality factors that correlate with motives and outcomes relevant to media use) are often included in the scope of U&G research. Media researchers argue that psychological factors are a salient aspect of motivations for communication and socialization via media as well as determinants of motivation to use certain types of media (e.g., Bryant & Oliver, 2009; Conway & Rubin, 1991; Lin, Sung, & Chen, 2016; Neate et al., 2017; Wober, 2013). Using the U&G approach and recent U&G studies, we aimed with this study (a) to explore motivations for TSV, (b) to investigate TV series-related social interactions on SNSs, (c) to demonstrate the effects of TSV motivations on social interactions, and (d) to examine the effects of psychological traits on the motivations for TSV. Understanding these aspects of TSV will help reveal the nature of this emerging hybrid media use at the intersection of traditional TV use and SNS use by yielding meaningful insights for both research areas.
2. Theoretical Framework and Hypotheses 2.1. Motivations for TSV experience 2
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U&G theory focuses on analyzing motivations and gratifications associated with individuals’ media use and communication behaviors. It explains how and why people use media (Katz, Blumler, & Gurevitch, 1974), assuming that individuals have innate needs that are gratified by media. Under the U&G perspective, TSV meets TV viewers’ needs; for instance, it provides users with opportunities to form or join buddy groups and share opinions and information about TV series with other two-screen viewers. However, we do not yet know which aspects of TSV users find gratifying? Broadly, the gratifications associated with traditional TV viewing are identified as the “water-cooler effect,” i.e., talking about last night’s programs with friends and coworkers around the water cooler (Lochrie & Coulton, 2012). The concept of the water cooler effect as a motivator illustrates that real-time TSV can be an extension of the motivators of traditional TVviewing behaviors. In this context, the issue of whether, how, and to what extent TV viewers’ motivations for TSV are similar to the motivators of traditional TV viewing warrants further investigation. In contrast, TSV users actively utilize SNSs as backchannels for social interaction (Shim et al., 2015; Van Es, 2016). Considering this feature, the motivations for TSV are likely to be indirectly or positively associated with the motivations for SNS use, and in recent years, many studies have investigated the motivations for SNS use. For example, previous studies have identified general motivations such as managing mood, surveillance, maintaining relationships, connecting with people with shared interests, coolness, and getting recognition (e.g., Choi, 2016; Lee and Lee, 2017; Sheldon & Bryant, 2016). When SNSs are combined with TV viewing, people seek a variety of gratifications; according to Viacom (2013), there are three: function, community, and play. Studies on tweets during TV series show that people use Twitter to post 3
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and share information about what they are watching (Giglietto & Selva, 2014; Heo et al., 2016; Shim et al., 2015). It is conceivable that two-screen viewers utilize SNSs to gain new information and to participate in networked discussions about TV programs (Gil de Zúñiga et al., 2015). Although the motivations to watch TV and use SNSs have been examined, the motivations for TSV remain unclear. Exploring what viewers seek from a hybrid of TV and SNSs will pave the way for more profound discussions on TSV. In that vein, we asked the following research question: RQ1: What are the motivations for engaging in TSV? 2.2. Relationship between Motivations for TSV and Social Interactions In traditional mass media research, media use has been commonly conceptualized as mere exposure to a medium and is often measured by the sheer amount or frequency of exposure. Many researchers have operationalized exposure as the amount of time a user spends online or the number of hours a user spends watching TV. Such a one-dimensional measure of media use originates from the passive nature of the traditional television experience. However, the emergence of multi-functional interactive media challenges this monolithic definition. TSV presents an exemplar case in which traditional media uses converge with social interactions on SNSs. In this study, we attempted to take a closer look at the social interactions of two-screen viewers on SNSs as an example of interactive media. Social interactions have been considered a crucial variable from the perspective of U&G researchers. According to the traditional media paradigm, these interactions reflect goal-directed, intentional, and selective media use by levels of media utility, intention, selectivity, and involvement (Blumler, 1979; Levy & Windahl, 1984). TSV exemplifies a new type of social4
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interaction behavior driven by the affordances offered by the new medium, such as the ability to share, communicate, and interact with others about TV series. For instance, a report by Nielsen (2017) revealed that 5.9 million U.S. TV viewers create an average of 11.8 million TV-related Facebook posts while watching TV per day. Thus, we proposed that these new social interactions extend existing social-interaction behaviors in the legacy media context. Based on these prior studies, we proposed the second research question as follows: RQ2: What social-interaction behaviors do TSV users show on SNSs? A number of U&G researchers (Levy & Windahl, 1984; Stevens, & Dillman Carpentier, 2017) have long posited that motivations for media use determine media-related behaviors. User motivations are associated with a range of social-interaction behaviors, including browsing and interacting with like-minded people (e.g., Karapanos, Teixeira, & Gouveia, 2016). With regard to interactive media, Haridakis and Hanson (2009) found that both user motivations and individual characteristics predict viewing and sharing behaviors on YouTube. Following this strand of literature, we anticipated in this study potentially significant and positive relationships between TSV motivations and TSV-related social interactions. Specifically, we proposed the following hypothesis: H1: Any of the motivations for TSV are positively associated with social interactions. 2.3.
Psychological Predictors of Motivation: Innovativeness, Behavioral Activation, and Behavioral Inhibition The U&G approach posits that people’s motivations for media use are influenced by such
specific psychological traits as innovativeness (e.g., Choi & Kim, 2016; Park et al, 2013), their behavioral activation systems (e.g., Böcking & Fahr, 2009; Park et al., 2013; Vangeel et al., 2016), and their behavioral inhibition systems (e.g., Böcking & Fahr, 2009; Vangeel et al., 2016). 5
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First, innovativeness, a trait that reflects an individual’s engagement in a new experience with more stimulating objects, has been repeatedly shown to have a significant and positive effect on the motivation to adopt new media (Choi & Kim, 2016; Park et al, 2013). Following this line of thought, previous research has demonstrated that innovativeness is closely related to SNS usage (Choi & Kim, 2016) and online social activity (Sun, et al., 2006). Such research suggests that innovativeness would be relevant to any motivation for engaging in TSV. Based on that discussion, we also proposed the following hypothesis: H2: A viewer’s innovativeness will be positively associated with any motivation for TSV. Gray (1990) theorized two conceptual motivational systems: the behavioral activation system (BAS), the mechanism that controls a person’s approach motivation and facilitates reward-based behaviors and the behavioral inhibition system (BIS), which manages avoidance motivation and inhibits behavior that could lead to punishment (e.g., Carver & White, 1994; Elliot & Thrash, 2002; Hirsh & Kang, 2016). The BAS and BIS assess individuals’ approach and avoidance tendencies and have produced a plethora of research; the BAS approach or tendency was found to be linked to extraversion and sensation seeking, and the BIS or aversive tendency was shown to be related to neuroticism (Ball & Zuckerman, 1990; Beullens, Rhodes, & Eggermont, 2016; Elliot & Thrash, 2002). In media research, the BAS, extraversion, sensation seeking, and the approach tendency have been found to be related to less television watching and more socializing (Böcking & Fahr, 2009; Finn, 1997) because extraverted or sensation-seeking people prefer talking with real people about media use. In contrast, the BIS, neurosis, or aversion-oriented individuals appear to choose TV over interacting with others because it produces anxiety (Böcking & Fahr, 2009; 6
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Greenwood, 2008). Because TSV combines watching TV and interacting with other people, predicting the relationship between the BAS and BIS and TSV motivations is problematic. Therefore, we generated a third research question: RQ 3: How are the BAS and BIS related to the motivations identified by RQ1? 3. Methods 3.1. Procedure and Sample In this study, we focused on individuals who watched TV and used SNSs on their smart phones while they watched. One audience rating survey in 2015 (Nielsen Korea, 2015) found that among all genres (e.g., entertainment, documentaries), participants ranked TV first through tenth. Because TV series are the most popular TV genre in South Korea, the respondents are likely representative of the general population. We collected our data from a representative sample of the South Korean population in metropolitan Seoul using online surveys; specifically, we selected respondents who reported using an SNS at least every two or three days and who had watched at least one episode of a TV series during the previous week, and we explained to them the concept of TSV. A total of 484 participants agreed to participate, and 442 respondents completed the survey; the final sample slightly over-represented females at 51.1%, and their ages ranged from 21 to 64 years (M=36.68, SD = 10.5). The descriptive statistics are shown in Table 1. 3.2.
Measurement 3.2.1. Motivation for TSV We constructed the TSV motivation measures based on the existing literature (Barker,
2009; Lin & Lu, 2011; Rubin & Rubin, 1982). We also included several of Leung and Wei’s (2000) measures of mobile phone motives, including “to enjoy the interactive element with 7
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others at any time about TV series” that were not included in previous SNS or TV motivation scales. These items focused on using mobile phones to facilitate SNS, and we asked respondents about the degree to which they agreed with each of 16 reasons1 for engaging in TSV such as “to talk with others to share feelings on TV series.” The responses were anchored on a seven-point Likert-scale ranging from one (not at all likely) to seven (extremely likely). 3.2.2. Social interactions on SNSs To capture the extent to which the respondents engaged in social interactions on SNSs related to TV series, we mainly referred to Hunt, Atkin, and Krishnan’s (2011) interactive features scale and adjusted it for our study. Social-interaction behaviors relate to posting and replying to other SNS users for open and evolved social interaction (Torrez-Riley, 2011). We also added a number of items that we adopted from previous research (Shim et al., 2015) to better represent SNS social interactions, specifically, 20 questions on the frequency of respondents’ social interactions on SNSs, for example, “How frequently do you express your emotions about TV series on SNSs?”; “How often do you read streams of comments posted by others?”; and “How frequently do you share information about TV series on SNSs?” The sevenpoint scale ranged from one (not at all) to seven (very often). 3.2.3. Innovativeness Unlike more general conceptualizations of innovativeness (e.g., Rogers, 2003), we measured the concept in the context of newer technologies using an innovativeness scale developed by Goldsmith and Hofacker (1991). Earlier, Roger’s innovativeness (1962) was criticized for theoretical reasons (Hunt et al., 1977; Midgley and Dowling, 1978). In theoretical criticism, time of adoption is a temporal concept that is equated with the construct 1
A complete list of the items and detailed wording are provided in the Results section. 8
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innovativeness, but it bears no isomorphic relationship with this latent construct it is supposed to operationalize (Midgely and Dowling 1978). The innovativeness measures we used in this study had been proven to be valid and reliable (see Goldsmith & Flynn, 1992; Haridakis & Hanson, 2009), resolving this theoretical argument. The six-item scale we used was domain specific and concentrated on innovativeness in products of specific areas of interest. The respondents indicated their level of agreement on a scale of one (strongly disagree) to seven (strongly agree) with items such as “I upgrade my computer in order to stay up-to-date at all times” and “I look for news about the development of new technologies or new electronic equipment.” The total Cronbach’s alpha for all six items was .88 (M = 4.84, SD = 0.39); that is, as in past research on innovativeness (e.g., Goldsmith & Flynn, 1992; Haridakis & Hanson, 2009), various components of innovativeness are internally consistent. We totaled the responses to the six items and calculated the average to create an innovativeness scale. 3.2.4. Behavioral Activation System and Behavioral Inhibition System We used Carver and White’s (1994) BAS and BIS scales to measure the respondents’ perceived appetitive/approach (i.e., BAS) and aversive/avoidance (i.e., BIS) motivations. The respondents indicated their levels of agreement with each of 12 items designed to assess BAS on a scale of one (strongly disagree) to seven (strongly agree), for example, “When I get something I want, I feel excited and energized,” “When I go after something, I use a ‘no holds barred’ approach,” and “I crave excitement and new sensations.” To build that scale, we summed the scores for the 12 items and calculated the average to create a BAS scale; the resulting scale had a mean of 4.95, with a standard deviation of 2.16. The Cronbach’s alpha for these 12 items was .90, which indicated that the scale was unidimensional. In the same vein, we asked the respondents to rate their levels of agreement with each of 9
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seven items designed to assess BIS on a scale of one (strongly disagree) to seven (strongly agree). The items on the BIS scale included “I worry about making mistakes,” “Even if something bad is about to happen to me, I rarely experience fear or nervousness,” and “I feel worried when I think I have done poorly at something.” We summed the responses to these items and averaged them to create a BIS scale, and the Cronbach’s alpha for the BIS scale was .91 (M = 4.67, SD = 1.64). 4. Results 4.1. Motivations for TSV and Social Interactions First, we examined the results (Table 2) of principal component analysis (PCA) to determine the nature of the respondents’ motivations for TSV. Our aim was to identify distinct motivational factors and items related to their dimensions. The Kaiser-Meyer-Olkin (KMO) statistic of sampling adequacy (KMO = .92) and Bartlett’s test of sphericity (χ2 = 4574.40, p < .001) indicated that the sample was adequate and that the correlations among the variables were suitable for analysis. The PCA produced three distinct motivational factors, which we evaluated using Kaiser’s criterion of eigenvalue 1.0 and a factor loading of more than 0.5, and the three factors accounted for 67% of the variance. Approximately half of the items that emerged represented the social co-viewing (SCV) motivation, five items represented engagement (EN), and two items represented passing time (PT); information seeking did not load as a distinct motivation in our factor analysis. As shown in Table 2, three factors showed clear underlying motivations associated with TSV experience, and the reliability of these measurement items was sufficiently high (SCV: Cronbach’s α = .92, EN: Cronbach’s α = .86, PT: Pearson’s r = .86) to demonstrate strong face validity. Three factors correlated with each other (SCV-PT: Pearson’s r = .28, p < .001; SCV-EN: Pearson’s r = .59, p < .001, PT-EN: Pearson’s r = .15, p < .001), suggesting that these motivations were complementary rather than mutually exclusive, as we 10
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noted earlier. Next, using the same procedure as for the motivations for TSV, we conducted a PCA for social interactions. Again, KMO = .95 and Bartlett’s test of sphericity found a χ2 of 6559.67 (p < .001), which indicated that PCA was suitable for our obtained samples, and we identified two social interaction dimensions (Table 3) that were associated with TSV. We labeled each factor based on both the meanings of and the common ground reflected by the items that loaded under each factor. Social sharing referred to active sharing behaviors such as posting and re-tweeting information about TV series or contacting actors or producers. Issue surveillance referred actions such as observing other viewers’ thoughts regarding, TV series these two factors jointly explained 63.45% of the total variance. The internal reliability scores of each factor were satisfactory (social sharing: Cronbach’s α = .95; issue surveillance: Cronbach’s α = .87) and were relatively highly correlated (Pearson’s r = .50, p < .001). 4.2. Psychological Traits, Motivations, and Social Interactions After we identified the TSV motivations and social-interaction behaviors, we examined the relationships among the motivations, social interactions, and psychological traits (Table 4). Columns 1 to 3 in Table 4 report the regression analysis results for the models that predicted the three motivations (i.e., SCV, EN, and PT) as focal dependent variables as a function of psychological traits (i.e., innovativeness, BAS, and BIS) and the demographic and media use covariates (i.e., age, gender, and time spent watching TV). The results showed that two of the three psychological traits had significant effects on motivational factors. Overall, after we controlled for demographic and media use variables (block 1; see the ΔR2 row in Table 4) in all models, the psychological traits (block 2) contributed significantly to increasing the explained variance of each of the focal dependent variables. 11
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Innovativeness and BAS had positive effects on all three motivations: SCV (b = .23, p < .001; b = .40, p < .001, respectively); EN (b = .36, p < .001; b = .19, p < .05, respectively); and PT (b = .13, p < .10; b = .30, p < .01, respectively). None of the motivations was significantly affected by BIS. The next set of analyses, shown in the last two columns of Table 4, reported the regression models that predicted social interactions as a function of standard demographic and media use controls (block 1), psychological traits (block 2), and drivers of TSV (block 3). For the model that predicted social sharing, both the psychological traits (block 2; ΔR2 = .11, p < .001) and the motivations (block 3; ΔR2= .18, p < .001) significantly increased the explained variance, to approximately 35% of the total variance of the dependent variable. Similarly, for the model that predicted issue surveillance, psychological traits (block 2) (ΔR2 = .11, p < .001) and motivations (block 3) (ΔR2 = .06, p < .001) significantly increased the model’s total explained variance. A closer examination of the results revealed some noteworthy patterns. First, among the three psychological traits, as Table 4 shows, although innovativeness and BAS were significantly associated with both social-interaction behaviors, BIS did not significantly affect either. Second, EN (b = .43, p < .001) and PT (b = .12, p < .01) were significantly associated with social sharing, whereas SCV was not. In contrast, for issue surveillance, SCV was the only significant motivations (block 3; b = .19, p < .01). 5. Discussion In this study, we investigated online users’ TSV motivations, social interactions, and psychological traits. First, we identified three motivations that underlie TSV: social co-viewing, passing time, and engagement. We classified the social-interaction behaviors into two categories, 12
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social sharing and issue surveillance, and passing time and engagement positively affected social sharing, whereas social co-viewing affected only issue surveillance. In addition, we found that innovativeness and BAS were positively associated with all three TSV motivations but that BIS was not. The results indicate that social co-viewing plays an important role in TSV. This implies that most two-screen viewers use their second screens to share their opinions and learn about others’ views on TV series, making all feel that they are watching the programs together. This finding underlines viewers’ desire to use TV socially by watching, sharing, and discussing programs with their SNS friends and fellow users. We can also infer that information exchange and sharing are fundamental to the co-viewing experience. In contrast, the role of passing time in this study appears to have been consistent with previous findings on TV viewers (Dias, 2016; Papacharissi & Rubin, 2000); despite the shift in TV consumption trends, passing time remained important for two-screen viewers. At the same time, two-screen viewers who reported engagement enjoyed the opportunity to engage in the programs they watch by means of social media. It appears that two-screen viewers exert greater control by simultaneously utilizing both traditional (TV) and new (SNSs) media rather than being mere passive recipients of ready-made programs offered by a single medium. This, in turn, has significant implications for the end of TV production: active involvement of TSV users is leading producers to adopt more open-ended conclusions. Indeed, a number of recent empirical studies have focused on how viewers directly share their ideas with TV production staff through the Internet (e.g., Cheng, Wu, & Chen, 2016; Godlewski & Perse, 2010; Shim et al., 2015). Our results also revealed the relationship between TSV motivations and social interactions. Through social sharing, two-screen viewers shared information and opinions about 13
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their favorite TV series with other fans; viewers observed others’ discussions and shared information and opinions about TV series through issue surveillance. Interestingly, our analyses indicated different patterns in the associations between TSV motivation and social-interactions. Passing time and engagement affected social sharing, whereas social co-viewing affected only issue surveillance. Although it is plausible to assume that viewers who rated social co-viewing highly shared more information and opinions, social co-viewing was not associated with social sharing. This finding adds implications for one of the primary theoretical propositions of the U&G framework: the effects of certain motivations on users’ behavior is equivocally consistent (e.g., Ferguson & Perse, 2000; Papacharissi & Rubin, 2000; Rigby & Ryan, 2016). However, the question of why social co-viewing as a motivation did not affect social sharing still remains to be accounted for. One possible explanation might be that high social coviewing viewers who desire to socialize with others are likely to hold back before they fulfill their desires. If that is the case, respondents who scored high for social co-viewing may turn to issue surveillance before directly engaging in social sharing because they may consider the former to be less risky than the latter. An alternative explanation considers the changing nature of SNS networks. In recent years, closed SNSs, within which people only communicate with friends, family members, and acquaintances, have become increasingly popular; in South Korea, the popular closed SNS KakaoTalk is used by 94.3% of smart phone users (DMC Media, 2016). When viewers want to share their opinions on TV programs with others, they are more likely to choose trustworthy and private closed-SNS networks over public networks such as Twitter or Facebook: Expressing opinions on closed-SNSs networks, on which people can choose whom to chat with, is associated with a lower psychological burden and greater security than doing so on open SNSs. Considering that the participants in this study responded based on open SNS usage, 14
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their social interaction responses might have shown greater issue surveillance than social sharing. That is, the relationship between social co-viewing as a motivation and social sharing might depend on the SNSs being used. Passing time and engagement affected social-sharing, our findings on passing time did not align with those defined by previous studies (e.g., Greenberg, 1974; Rubin, 1981, 1983). According to the previous studies, TV viewing allowed a passive type of passing time, but TSV social-sharing in this study resulted from passing time. It is conceivable that the nature of passing time in the hybrid media environment can turn passive motivation unrelated to purposeful or instrumental goals into active motivation for passing time. Engagement also affected social-sharing. The viewers who reported high engagement shared messages about programs to boost TV ratings or express their opinions to program producers using the communication tools offered by SNSs. That is, those with high engagement as a motivation might more often share messages about TV series. Lastly, we investigated the effects of psychological traits and found that viewers’ innovativeness had a significant effect on all three TSV motivations. Highly innovative viewers appeared to participate more vigorously in discourse about programs or exchange their opinions and information about TV series with others. Our results illustrate that innovative viewers engage in TSV as a new way to pass time or rest. The BAS also helped to explain all motivations for TSV. These results conform to the existing literature (Depue & Iacono, 1989) in which engaging in TV series was considered an intentional, goal-oriented behavior influenced by BAS. In the current study, we demonstrated that those with a high BAS tended to relieve boredom and passing time; however, unlike innovativeness and the BAS, the BIS did not affect any TSV motivations. Furthermore, viewers with a high BIS may feel anxious or nervous about interacting 15
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with others and sharing questions as well as engaging in TV series as a purposeful behavior. However, future research should verify the possible associations between BIS and passing time. In sum, our findings indicate although TSV lies on a continuum with traditional TV viewing, new communication technologies and media services are transforming the TV-viewing experience. Users actively incorporate SNSs in various ways according to their motivations, which leads them to different types of TSV behaviors. Unlike with traditional TV viewing, TSV users actively seek the networking opportunities offered by new social platforms to enhance their viewing experiences. Most of all, being social stands out as a hallmark of the TSV experience in that social media play the role of connecting otherwise isolated viewers. That is, TV would be becoming social again through convergent media such as SNSs. Despite its merits, this study is not without limitations. In our earlier review of TSV, SNSs on smart phones were a back channel in the context of TV viewing, but the reverse could also become a trend given that SNS use on smart phones has become a primary behavior and TV watching a background behavior. Second, in this study, we explored only one TV genre. Future research should include a more diverse range of genres such as news (e.g., Gil de Zúñiga et al., 2015), reality shows, and talk shows. Third, we paid only limited attention to the role of psychological traits; for example, the psychological traits of two-screen viewers might be moderators or mediators rather than direct antecedents of TSV motivations. Fourth, the results of the present study are not sufficient to confirm whether purposeful viewers are more active in their pre-viewing or post-viewing planning than their counterparts who watch TV out of habit. Further research should consider a broader spectrum of time dimensions of social interactions, e.g., pre-viewing (Hess et al., 2012), during viewing (Wohn & Na, 2011), and post-viewing (Giglietto & Selva, 2014) activity. 16
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Table 1. Descriptive Statistics of the Participants Gender Male Female Age 20-29 30-39 40-49 50-59 60-69 Education level High school graduate or less College student College graduate Advanced degree over BA Monthly income < $2,000 $2,000-$3,000 $3,000-$4,000 $4,000-$5,000 $5,000 < TV viewing (per a day) 1-2 hours 2-3 hours 3 hours < SNS use (1: rarely use - 7: use always)
Frequency
Percentage
216 226
48.9% 51.1%
137 140 99 55 11
31.0% 31.7% 22.4% 12.4% 2.5%
40 38 318 46
9.0% 8.6% 71.9% 10.4%
15 70 98 100 159
3.4% 15.8% 22.2% 22.6% 36.0%
70 15.8% 130 29.4% 242 54.8% Mean = 4.27 (SD = .69)
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Table 2. Principal Component Analysis Results for TSV Motivations Items
Components SCV EN
PT
To share my opinion on TV series with others
.844
.057
-.107
To interact with others
.834
-.004
.007
To chat with friends while watching TV
.758
-.110
.190
To share my real-time questions and thoughts on TV series with others during the TV series To have fun
.755
.123
-.124
.741
-.180
.317
To socialize with others
.693
.062
.106
To find out others’ views and thoughts
.666
.172
-.197
To share a sense of realism and suspense with others
.602
.249
.016
To watch TV together
.583
.289
.019
-.093
.851
.114
To boost the ratings of my favorite TV series
.028
.769
.184
To acquire new information about various TV series
.137
.746
-.074
To get information about programs I like
.222
.639
-.010
To help keep TV series I like on the air for many years to come
.366
.543
-.014
To relieve a sense of boredom
.131
.029
.875
-.023
.248
.848
8.05
1.16
1.52
50.28
7.22
9.49
.92
.86
.86
To put across my views or opinions to producers of TV series
To pass the time by watching TV Eigenvalue % Variance explained Cronbach’s α
Note: Principal component analysis with direct oblimin was used. SCV: Social co-viewing, EN: Engagement, PT: Passing time.
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Table 3. Principal Component Analysis Results for Social Interactions “On SNSs, I…”
Components Social sharing Issue surveillance
Post blogs or news articles about TV series or actors
.922
-.175
Post photos or videos about TV series or actors
.917
-.212
Send messages to people who have posted opinions on or information about TV series or actors Visit SNS pages of TV series or actors to post my opinions
.892
-.129
.871
-.114
Receive messages about TV series or actors
.753
.022
Send messages to friends or followers about TV series or actors Post opinions on TV series
.748
.007
.737
.113
Re-tweet or share others’ opinions or information about TV series or actors Follow accounts of TV series or actors
.728
.139
.715
.074
Receive suggestions for TV series from friends
.709
.154
Reply to or comment on others’ opinions on or information about TV series or actors Recommend TV series to friends
.692
.145
.640
.217
Give suggestions for TV series to friends
.624
.240
Receive recommendations for TV series from friends
.619
.271
Click “like” or “add to favorites” button on other people’s opinions on or information about TV series or actors View others’ photos or videos of TV series or actors
.525
.312
-.088
.880
Read articles or posts about TV series or actors through links
-.009
.836
View others’ opinions of TV series
.054
.762
Pay attention to articles, photos, or videos about TV series or actors
.202
.677
Read opinions on or information about TV series or actors through RT or sharing Eigenvalue % Variance explained Cronbach’s α Note. Principal component analysis with direct oblimin was used.
.310
.601
10.76 53.78 .95
1.93 9.67 .87
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Table 4. OLS Regressions Results for Predictors of Motivation and Social Interactions Motivation SCV Block 1. Controls (Intercept) Sociodemographic Age Gender (female = 0) Education level Income level Media Use TV viewing SNS use 2 R Block 2. Psychological Traits Innovativeness BAS BIS ΔR2 Block 3. Motivations SCV PT EN ΔR2 Total R2 F (df1, df2)
EN
Social interactions PT
SSB
ISB
3.71 (.63) *** 2.66 (.71) *** 3.73 (.82) ***
1.48 (.73) *
2.86 (.65) ***
-.01 (.01) **
.00 (.01)
-.01 (.01)
-.01 (.01)
.00 (.01)
-.04 (.1)
.08 (.12)
.15 (.13)
.04 (.12)
-.18 (.11) #
-.05 (.07) .04 (.04)
-.03 (.08) .03 (.04)
-.14 (.10) .01 (.05)
.03 (.08) .12 (.05) **
.11 (.08) .05 (.04)
.12 (.07) # .16 (.08) * .04 **
.18 (.08) * .11 (.09) .02 **
.18 (.09) * .04 (.10) .02 **
.28 (.08) *** .12 (.09) .06 ***
.22 (.07) ** .09 (.08) .05 **
.23 (.06) *** .40 (.07) *** .04 (.04) .24 ***
.36 (.07) *** .19 (.08) * .08 (.05) .18 ***
.13 (.08) # .30 (.10) ** .09 (.06) .08 ***
.25 (.07) *** .26 (.09) ** -.08 (.05) .11 ***
.18 (.06) ** .27 (.08) *** .05 (.05) .11 ***
.10 ***
-.01 (.07) .12 (.04) ** .43 (.06) *** .18 *** .35 ***
.19 (.07)** .06 (.04) .06 (.06) .06 *** .22***
.28 ***
.20 ***
18.22 (9, 432) 11.81 (9, 432) 5.13 (9,432)
19.24 (12, 429) 9.80 (12, 429)
Note. Coefficients are non-standardized OLS regression coefficients with standard error in parenthesis. SCV: Social co-viewing; EN: Engagement; PT: Passing time; SSB: Social sharing; ISB: Issue surveillance. N=442. # p < .1, * p < .05, ** p < .01, *** p < .001
Hongjin Shim, Euikyung Shin, Sohye Lim PII:
S0747-5632(17)30331-X
DOI:
10.1016/j.chb.2017.05.019
Reference:
CHB 4981
To appear in:
Computers in Human Behavior
Received Date:
13 June 2016
Revised Date:
10 May 2017
Accepted Date:
11 May 2017
Please cite this article as: Hongjin Shim, Euikyung Shin, Sohye Lim, What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions, Computers in Human Behavior (2017), doi: 10.1016/j.chb.2017.05.019
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Three primary TSV motivations were identified: social co-viewing, engagement, and passing time. Social interactions were categorized into social sharing and issue surveillance.. Engagement and passing time were associated with social sharing. Innovativeness and BAS had significant impacts on TSV users’ motivations.
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What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions
Hongjin Shim, Ph.D. Research Fellow, Broadcasting Media Research Division Korea Information Society Development Institute 18 Jeongtong-ro, Deoksan-myeon, Jincheon-gun, Chungcheongbuk-do, 365-841, South Korea 82-02-570-4260 [email protected] Euikyung Shin, Ph.D. Candidate Graduate School of Communication and Arts Yonsei University #208 Billingsley Hall, 50 Yonsei-ro, Seodaemun-Gu, Seoul, South Korea 82-10-2956-1809 [email protected] Sohye Lim, Ph.D. (corresponding author) Associate Professor, School of Communication and Media Ewha Womans University Seodaemun-Gu Daehyundong 11-1, Seoul, South Korea 82-10-8669-8901 [email protected]
Manuscript resubmitted to Computers in Human Behavior for a review
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What makes us two-screen users? The effects of two-screen viewing motivation and psychological traits on social interactions
1. Introduction The experience of TV viewing has long been regarded as one-way communication in which viewers passively receive what the media broadcasts (Thompson, 1995; Torrez-Riley, 2011). The influx of SNSs and mobile technology provides TV viewers with unprecedented opportunities for communication with others in a variety of ways (Hill & Benton, 2012; Neate, Jones, & Evans, 2017). Specifically, viewers simultaneously use multiple screens: the fixed TV screens on which they view the TV series that lead them to social interactions such as sharing opinions or recommending programs and the screens and devices (e.g., smart phones) that viewers use as backchannels to interact with others remotely on SNSs (Kroon, 2017; Shim et al, 2015). This new TV viewing behavior, which embodies one aspect of the phenomenon known as hybrid media, is called ‘‘two-screen viewing’’ or “second screening” (Choi & Jung, 2016; Johns, 2012). This two-screen viewing (TSV) phenomenon is becoming increasingly prevalent; according to a recent report, 21% of tablet users and 30% of smart phone users in the United States use their smart devices while watching TV multiple times a day (Nielson, 2017). Similarly, DMC Media (2017) found in its survey conducted in Korea that 92.8% of respondents had used second screens while watching TV. Despite the prominence of two-screen viewing, key questions remain regarding the antecedents and effects of TSV including: What motivations drive people to participate in TSV? Which user traits influence those motivations? How are those motivations related to actual TSV patterns? Addressing these questions is important for 1
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improving not only our understanding of how people consume TV is evolving but also the way TV viewers are actively interweaving social media into their traditional media use. To investigate these questions, we adopted the uses and gratification approach (U&G), which provides a useful theoretical framework to explore the motivations for TSV. Previous research based on the U&G frame primarily investigated the role of viewers’ needs and expectations in mass communication (e.g., Papacharissi & Mendelson, 2011; Rubin, 1983). For example, numerous studies have shown relaxation, habit, learning, escape, information-seeking, arousal, and entertainment to be the salient gratifications derived from watching TV (Greenberg, 1974; Rubin, 1981; 1983). In addition, studies on the social and psychological origins of needs (e.g., personality factors that correlate with motives and outcomes relevant to media use) are often included in the scope of U&G research. Media researchers argue that psychological factors are a salient aspect of motivations for communication and socialization via media as well as determinants of motivation to use certain types of media (e.g., Bryant & Oliver, 2009; Conway & Rubin, 1991; Lin, Sung, & Chen, 2016; Neate et al., 2017; Wober, 2013). Using the U&G approach and recent U&G studies, we aimed with this study (a) to explore motivations for TSV, (b) to investigate TV series-related social interactions on SNSs, (c) to demonstrate the effects of TSV motivations on social interactions, and (d) to examine the effects of psychological traits on the motivations for TSV. Understanding these aspects of TSV will help reveal the nature of this emerging hybrid media use at the intersection of traditional TV use and SNS use by yielding meaningful insights for both research areas.
2. Theoretical Framework and Hypotheses 2.1. Motivations for TSV experience 2
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U&G theory focuses on analyzing motivations and gratifications associated with individuals’ media use and communication behaviors. It explains how and why people use media (Katz, Blumler, & Gurevitch, 1974), assuming that individuals have innate needs that are gratified by media. Under the U&G perspective, TSV meets TV viewers’ needs; for instance, it provides users with opportunities to form or join buddy groups and share opinions and information about TV series with other two-screen viewers. However, we do not yet know which aspects of TSV users find gratifying? Broadly, the gratifications associated with traditional TV viewing are identified as the “water-cooler effect,” i.e., talking about last night’s programs with friends and coworkers around the water cooler (Lochrie & Coulton, 2012). The concept of the water cooler effect as a motivator illustrates that real-time TSV can be an extension of the motivators of traditional TVviewing behaviors. In this context, the issue of whether, how, and to what extent TV viewers’ motivations for TSV are similar to the motivators of traditional TV viewing warrants further investigation. In contrast, TSV users actively utilize SNSs as backchannels for social interaction (Shim et al., 2015; Van Es, 2016). Considering this feature, the motivations for TSV are likely to be indirectly or positively associated with the motivations for SNS use, and in recent years, many studies have investigated the motivations for SNS use. For example, previous studies have identified general motivations such as managing mood, surveillance, maintaining relationships, connecting with people with shared interests, coolness, and getting recognition (e.g., Choi, 2016; Lee and Lee, 2017; Sheldon & Bryant, 2016). When SNSs are combined with TV viewing, people seek a variety of gratifications; according to Viacom (2013), there are three: function, community, and play. Studies on tweets during TV series show that people use Twitter to post 3
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and share information about what they are watching (Giglietto & Selva, 2014; Heo et al., 2016; Shim et al., 2015). It is conceivable that two-screen viewers utilize SNSs to gain new information and to participate in networked discussions about TV programs (Gil de Zúñiga et al., 2015). Although the motivations to watch TV and use SNSs have been examined, the motivations for TSV remain unclear. Exploring what viewers seek from a hybrid of TV and SNSs will pave the way for more profound discussions on TSV. In that vein, we asked the following research question: RQ1: What are the motivations for engaging in TSV? 2.2. Relationship between Motivations for TSV and Social Interactions In traditional mass media research, media use has been commonly conceptualized as mere exposure to a medium and is often measured by the sheer amount or frequency of exposure. Many researchers have operationalized exposure as the amount of time a user spends online or the number of hours a user spends watching TV. Such a one-dimensional measure of media use originates from the passive nature of the traditional television experience. However, the emergence of multi-functional interactive media challenges this monolithic definition. TSV presents an exemplar case in which traditional media uses converge with social interactions on SNSs. In this study, we attempted to take a closer look at the social interactions of two-screen viewers on SNSs as an example of interactive media. Social interactions have been considered a crucial variable from the perspective of U&G researchers. According to the traditional media paradigm, these interactions reflect goal-directed, intentional, and selective media use by levels of media utility, intention, selectivity, and involvement (Blumler, 1979; Levy & Windahl, 1984). TSV exemplifies a new type of social4
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interaction behavior driven by the affordances offered by the new medium, such as the ability to share, communicate, and interact with others about TV series. For instance, a report by Nielsen (2017) revealed that 5.9 million U.S. TV viewers create an average of 11.8 million TV-related Facebook posts while watching TV per day. Thus, we proposed that these new social interactions extend existing social-interaction behaviors in the legacy media context. Based on these prior studies, we proposed the second research question as follows: RQ2: What social-interaction behaviors do TSV users show on SNSs? A number of U&G researchers (Levy & Windahl, 1984; Stevens, & Dillman Carpentier, 2017) have long posited that motivations for media use determine media-related behaviors. User motivations are associated with a range of social-interaction behaviors, including browsing and interacting with like-minded people (e.g., Karapanos, Teixeira, & Gouveia, 2016). With regard to interactive media, Haridakis and Hanson (2009) found that both user motivations and individual characteristics predict viewing and sharing behaviors on YouTube. Following this strand of literature, we anticipated in this study potentially significant and positive relationships between TSV motivations and TSV-related social interactions. Specifically, we proposed the following hypothesis: H1: Any of the motivations for TSV are positively associated with social interactions. 2.3.
Psychological Predictors of Motivation: Innovativeness, Behavioral Activation, and Behavioral Inhibition The U&G approach posits that people’s motivations for media use are influenced by such
specific psychological traits as innovativeness (e.g., Choi & Kim, 2016; Park et al, 2013), their behavioral activation systems (e.g., Böcking & Fahr, 2009; Park et al., 2013; Vangeel et al., 2016), and their behavioral inhibition systems (e.g., Böcking & Fahr, 2009; Vangeel et al., 2016). 5
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First, innovativeness, a trait that reflects an individual’s engagement in a new experience with more stimulating objects, has been repeatedly shown to have a significant and positive effect on the motivation to adopt new media (Choi & Kim, 2016; Park et al, 2013). Following this line of thought, previous research has demonstrated that innovativeness is closely related to SNS usage (Choi & Kim, 2016) and online social activity (Sun, et al., 2006). Such research suggests that innovativeness would be relevant to any motivation for engaging in TSV. Based on that discussion, we also proposed the following hypothesis: H2: A viewer’s innovativeness will be positively associated with any motivation for TSV. Gray (1990) theorized two conceptual motivational systems: the behavioral activation system (BAS), the mechanism that controls a person’s approach motivation and facilitates reward-based behaviors and the behavioral inhibition system (BIS), which manages avoidance motivation and inhibits behavior that could lead to punishment (e.g., Carver & White, 1994; Elliot & Thrash, 2002; Hirsh & Kang, 2016). The BAS and BIS assess individuals’ approach and avoidance tendencies and have produced a plethora of research; the BAS approach or tendency was found to be linked to extraversion and sensation seeking, and the BIS or aversive tendency was shown to be related to neuroticism (Ball & Zuckerman, 1990; Beullens, Rhodes, & Eggermont, 2016; Elliot & Thrash, 2002). In media research, the BAS, extraversion, sensation seeking, and the approach tendency have been found to be related to less television watching and more socializing (Böcking & Fahr, 2009; Finn, 1997) because extraverted or sensation-seeking people prefer talking with real people about media use. In contrast, the BIS, neurosis, or aversion-oriented individuals appear to choose TV over interacting with others because it produces anxiety (Böcking & Fahr, 2009; 6
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Greenwood, 2008). Because TSV combines watching TV and interacting with other people, predicting the relationship between the BAS and BIS and TSV motivations is problematic. Therefore, we generated a third research question: RQ 3: How are the BAS and BIS related to the motivations identified by RQ1? 3. Methods 3.1. Procedure and Sample In this study, we focused on individuals who watched TV and used SNSs on their smart phones while they watched. One audience rating survey in 2015 (Nielsen Korea, 2015) found that among all genres (e.g., entertainment, documentaries), participants ranked TV first through tenth. Because TV series are the most popular TV genre in South Korea, the respondents are likely representative of the general population. We collected our data from a representative sample of the South Korean population in metropolitan Seoul using online surveys; specifically, we selected respondents who reported using an SNS at least every two or three days and who had watched at least one episode of a TV series during the previous week, and we explained to them the concept of TSV. A total of 484 participants agreed to participate, and 442 respondents completed the survey; the final sample slightly over-represented females at 51.1%, and their ages ranged from 21 to 64 years (M=36.68, SD = 10.5). The descriptive statistics are shown in Table 1. 3.2.
Measurement 3.2.1. Motivation for TSV We constructed the TSV motivation measures based on the existing literature (Barker,
2009; Lin & Lu, 2011; Rubin & Rubin, 1982). We also included several of Leung and Wei’s (2000) measures of mobile phone motives, including “to enjoy the interactive element with 7
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others at any time about TV series” that were not included in previous SNS or TV motivation scales. These items focused on using mobile phones to facilitate SNS, and we asked respondents about the degree to which they agreed with each of 16 reasons1 for engaging in TSV such as “to talk with others to share feelings on TV series.” The responses were anchored on a seven-point Likert-scale ranging from one (not at all likely) to seven (extremely likely). 3.2.2. Social interactions on SNSs To capture the extent to which the respondents engaged in social interactions on SNSs related to TV series, we mainly referred to Hunt, Atkin, and Krishnan’s (2011) interactive features scale and adjusted it for our study. Social-interaction behaviors relate to posting and replying to other SNS users for open and evolved social interaction (Torrez-Riley, 2011). We also added a number of items that we adopted from previous research (Shim et al., 2015) to better represent SNS social interactions, specifically, 20 questions on the frequency of respondents’ social interactions on SNSs, for example, “How frequently do you express your emotions about TV series on SNSs?”; “How often do you read streams of comments posted by others?”; and “How frequently do you share information about TV series on SNSs?” The sevenpoint scale ranged from one (not at all) to seven (very often). 3.2.3. Innovativeness Unlike more general conceptualizations of innovativeness (e.g., Rogers, 2003), we measured the concept in the context of newer technologies using an innovativeness scale developed by Goldsmith and Hofacker (1991). Earlier, Roger’s innovativeness (1962) was criticized for theoretical reasons (Hunt et al., 1977; Midgley and Dowling, 1978). In theoretical criticism, time of adoption is a temporal concept that is equated with the construct 1
A complete list of the items and detailed wording are provided in the Results section. 8
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innovativeness, but it bears no isomorphic relationship with this latent construct it is supposed to operationalize (Midgely and Dowling 1978). The innovativeness measures we used in this study had been proven to be valid and reliable (see Goldsmith & Flynn, 1992; Haridakis & Hanson, 2009), resolving this theoretical argument. The six-item scale we used was domain specific and concentrated on innovativeness in products of specific areas of interest. The respondents indicated their level of agreement on a scale of one (strongly disagree) to seven (strongly agree) with items such as “I upgrade my computer in order to stay up-to-date at all times” and “I look for news about the development of new technologies or new electronic equipment.” The total Cronbach’s alpha for all six items was .88 (M = 4.84, SD = 0.39); that is, as in past research on innovativeness (e.g., Goldsmith & Flynn, 1992; Haridakis & Hanson, 2009), various components of innovativeness are internally consistent. We totaled the responses to the six items and calculated the average to create an innovativeness scale. 3.2.4. Behavioral Activation System and Behavioral Inhibition System We used Carver and White’s (1994) BAS and BIS scales to measure the respondents’ perceived appetitive/approach (i.e., BAS) and aversive/avoidance (i.e., BIS) motivations. The respondents indicated their levels of agreement with each of 12 items designed to assess BAS on a scale of one (strongly disagree) to seven (strongly agree), for example, “When I get something I want, I feel excited and energized,” “When I go after something, I use a ‘no holds barred’ approach,” and “I crave excitement and new sensations.” To build that scale, we summed the scores for the 12 items and calculated the average to create a BAS scale; the resulting scale had a mean of 4.95, with a standard deviation of 2.16. The Cronbach’s alpha for these 12 items was .90, which indicated that the scale was unidimensional. In the same vein, we asked the respondents to rate their levels of agreement with each of 9
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seven items designed to assess BIS on a scale of one (strongly disagree) to seven (strongly agree). The items on the BIS scale included “I worry about making mistakes,” “Even if something bad is about to happen to me, I rarely experience fear or nervousness,” and “I feel worried when I think I have done poorly at something.” We summed the responses to these items and averaged them to create a BIS scale, and the Cronbach’s alpha for the BIS scale was .91 (M = 4.67, SD = 1.64). 4. Results 4.1. Motivations for TSV and Social Interactions First, we examined the results (Table 2) of principal component analysis (PCA) to determine the nature of the respondents’ motivations for TSV. Our aim was to identify distinct motivational factors and items related to their dimensions. The Kaiser-Meyer-Olkin (KMO) statistic of sampling adequacy (KMO = .92) and Bartlett’s test of sphericity (χ2 = 4574.40, p < .001) indicated that the sample was adequate and that the correlations among the variables were suitable for analysis. The PCA produced three distinct motivational factors, which we evaluated using Kaiser’s criterion of eigenvalue 1.0 and a factor loading of more than 0.5, and the three factors accounted for 67% of the variance. Approximately half of the items that emerged represented the social co-viewing (SCV) motivation, five items represented engagement (EN), and two items represented passing time (PT); information seeking did not load as a distinct motivation in our factor analysis. As shown in Table 2, three factors showed clear underlying motivations associated with TSV experience, and the reliability of these measurement items was sufficiently high (SCV: Cronbach’s α = .92, EN: Cronbach’s α = .86, PT: Pearson’s r = .86) to demonstrate strong face validity. Three factors correlated with each other (SCV-PT: Pearson’s r = .28, p < .001; SCV-EN: Pearson’s r = .59, p < .001, PT-EN: Pearson’s r = .15, p < .001), suggesting that these motivations were complementary rather than mutually exclusive, as we 10
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noted earlier. Next, using the same procedure as for the motivations for TSV, we conducted a PCA for social interactions. Again, KMO = .95 and Bartlett’s test of sphericity found a χ2 of 6559.67 (p < .001), which indicated that PCA was suitable for our obtained samples, and we identified two social interaction dimensions (Table 3) that were associated with TSV. We labeled each factor based on both the meanings of and the common ground reflected by the items that loaded under each factor. Social sharing referred to active sharing behaviors such as posting and re-tweeting information about TV series or contacting actors or producers. Issue surveillance referred actions such as observing other viewers’ thoughts regarding, TV series these two factors jointly explained 63.45% of the total variance. The internal reliability scores of each factor were satisfactory (social sharing: Cronbach’s α = .95; issue surveillance: Cronbach’s α = .87) and were relatively highly correlated (Pearson’s r = .50, p < .001). 4.2. Psychological Traits, Motivations, and Social Interactions After we identified the TSV motivations and social-interaction behaviors, we examined the relationships among the motivations, social interactions, and psychological traits (Table 4). Columns 1 to 3 in Table 4 report the regression analysis results for the models that predicted the three motivations (i.e., SCV, EN, and PT) as focal dependent variables as a function of psychological traits (i.e., innovativeness, BAS, and BIS) and the demographic and media use covariates (i.e., age, gender, and time spent watching TV). The results showed that two of the three psychological traits had significant effects on motivational factors. Overall, after we controlled for demographic and media use variables (block 1; see the ΔR2 row in Table 4) in all models, the psychological traits (block 2) contributed significantly to increasing the explained variance of each of the focal dependent variables. 11
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Innovativeness and BAS had positive effects on all three motivations: SCV (b = .23, p < .001; b = .40, p < .001, respectively); EN (b = .36, p < .001; b = .19, p < .05, respectively); and PT (b = .13, p < .10; b = .30, p < .01, respectively). None of the motivations was significantly affected by BIS. The next set of analyses, shown in the last two columns of Table 4, reported the regression models that predicted social interactions as a function of standard demographic and media use controls (block 1), psychological traits (block 2), and drivers of TSV (block 3). For the model that predicted social sharing, both the psychological traits (block 2; ΔR2 = .11, p < .001) and the motivations (block 3; ΔR2= .18, p < .001) significantly increased the explained variance, to approximately 35% of the total variance of the dependent variable. Similarly, for the model that predicted issue surveillance, psychological traits (block 2) (ΔR2 = .11, p < .001) and motivations (block 3) (ΔR2 = .06, p < .001) significantly increased the model’s total explained variance. A closer examination of the results revealed some noteworthy patterns. First, among the three psychological traits, as Table 4 shows, although innovativeness and BAS were significantly associated with both social-interaction behaviors, BIS did not significantly affect either. Second, EN (b = .43, p < .001) and PT (b = .12, p < .01) were significantly associated with social sharing, whereas SCV was not. In contrast, for issue surveillance, SCV was the only significant motivations (block 3; b = .19, p < .01). 5. Discussion In this study, we investigated online users’ TSV motivations, social interactions, and psychological traits. First, we identified three motivations that underlie TSV: social co-viewing, passing time, and engagement. We classified the social-interaction behaviors into two categories, 12
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social sharing and issue surveillance, and passing time and engagement positively affected social sharing, whereas social co-viewing affected only issue surveillance. In addition, we found that innovativeness and BAS were positively associated with all three TSV motivations but that BIS was not. The results indicate that social co-viewing plays an important role in TSV. This implies that most two-screen viewers use their second screens to share their opinions and learn about others’ views on TV series, making all feel that they are watching the programs together. This finding underlines viewers’ desire to use TV socially by watching, sharing, and discussing programs with their SNS friends and fellow users. We can also infer that information exchange and sharing are fundamental to the co-viewing experience. In contrast, the role of passing time in this study appears to have been consistent with previous findings on TV viewers (Dias, 2016; Papacharissi & Rubin, 2000); despite the shift in TV consumption trends, passing time remained important for two-screen viewers. At the same time, two-screen viewers who reported engagement enjoyed the opportunity to engage in the programs they watch by means of social media. It appears that two-screen viewers exert greater control by simultaneously utilizing both traditional (TV) and new (SNSs) media rather than being mere passive recipients of ready-made programs offered by a single medium. This, in turn, has significant implications for the end of TV production: active involvement of TSV users is leading producers to adopt more open-ended conclusions. Indeed, a number of recent empirical studies have focused on how viewers directly share their ideas with TV production staff through the Internet (e.g., Cheng, Wu, & Chen, 2016; Godlewski & Perse, 2010; Shim et al., 2015). Our results also revealed the relationship between TSV motivations and social interactions. Through social sharing, two-screen viewers shared information and opinions about 13
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their favorite TV series with other fans; viewers observed others’ discussions and shared information and opinions about TV series through issue surveillance. Interestingly, our analyses indicated different patterns in the associations between TSV motivation and social-interactions. Passing time and engagement affected social sharing, whereas social co-viewing affected only issue surveillance. Although it is plausible to assume that viewers who rated social co-viewing highly shared more information and opinions, social co-viewing was not associated with social sharing. This finding adds implications for one of the primary theoretical propositions of the U&G framework: the effects of certain motivations on users’ behavior is equivocally consistent (e.g., Ferguson & Perse, 2000; Papacharissi & Rubin, 2000; Rigby & Ryan, 2016). However, the question of why social co-viewing as a motivation did not affect social sharing still remains to be accounted for. One possible explanation might be that high social coviewing viewers who desire to socialize with others are likely to hold back before they fulfill their desires. If that is the case, respondents who scored high for social co-viewing may turn to issue surveillance before directly engaging in social sharing because they may consider the former to be less risky than the latter. An alternative explanation considers the changing nature of SNS networks. In recent years, closed SNSs, within which people only communicate with friends, family members, and acquaintances, have become increasingly popular; in South Korea, the popular closed SNS KakaoTalk is used by 94.3% of smart phone users (DMC Media, 2016). When viewers want to share their opinions on TV programs with others, they are more likely to choose trustworthy and private closed-SNS networks over public networks such as Twitter or Facebook: Expressing opinions on closed-SNSs networks, on which people can choose whom to chat with, is associated with a lower psychological burden and greater security than doing so on open SNSs. Considering that the participants in this study responded based on open SNS usage, 14
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their social interaction responses might have shown greater issue surveillance than social sharing. That is, the relationship between social co-viewing as a motivation and social sharing might depend on the SNSs being used. Passing time and engagement affected social-sharing, our findings on passing time did not align with those defined by previous studies (e.g., Greenberg, 1974; Rubin, 1981, 1983). According to the previous studies, TV viewing allowed a passive type of passing time, but TSV social-sharing in this study resulted from passing time. It is conceivable that the nature of passing time in the hybrid media environment can turn passive motivation unrelated to purposeful or instrumental goals into active motivation for passing time. Engagement also affected social-sharing. The viewers who reported high engagement shared messages about programs to boost TV ratings or express their opinions to program producers using the communication tools offered by SNSs. That is, those with high engagement as a motivation might more often share messages about TV series. Lastly, we investigated the effects of psychological traits and found that viewers’ innovativeness had a significant effect on all three TSV motivations. Highly innovative viewers appeared to participate more vigorously in discourse about programs or exchange their opinions and information about TV series with others. Our results illustrate that innovative viewers engage in TSV as a new way to pass time or rest. The BAS also helped to explain all motivations for TSV. These results conform to the existing literature (Depue & Iacono, 1989) in which engaging in TV series was considered an intentional, goal-oriented behavior influenced by BAS. In the current study, we demonstrated that those with a high BAS tended to relieve boredom and passing time; however, unlike innovativeness and the BAS, the BIS did not affect any TSV motivations. Furthermore, viewers with a high BIS may feel anxious or nervous about interacting 15
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with others and sharing questions as well as engaging in TV series as a purposeful behavior. However, future research should verify the possible associations between BIS and passing time. In sum, our findings indicate although TSV lies on a continuum with traditional TV viewing, new communication technologies and media services are transforming the TV-viewing experience. Users actively incorporate SNSs in various ways according to their motivations, which leads them to different types of TSV behaviors. Unlike with traditional TV viewing, TSV users actively seek the networking opportunities offered by new social platforms to enhance their viewing experiences. Most of all, being social stands out as a hallmark of the TSV experience in that social media play the role of connecting otherwise isolated viewers. That is, TV would be becoming social again through convergent media such as SNSs. Despite its merits, this study is not without limitations. In our earlier review of TSV, SNSs on smart phones were a back channel in the context of TV viewing, but the reverse could also become a trend given that SNS use on smart phones has become a primary behavior and TV watching a background behavior. Second, in this study, we explored only one TV genre. Future research should include a more diverse range of genres such as news (e.g., Gil de Zúñiga et al., 2015), reality shows, and talk shows. Third, we paid only limited attention to the role of psychological traits; for example, the psychological traits of two-screen viewers might be moderators or mediators rather than direct antecedents of TSV motivations. Fourth, the results of the present study are not sufficient to confirm whether purposeful viewers are more active in their pre-viewing or post-viewing planning than their counterparts who watch TV out of habit. Further research should consider a broader spectrum of time dimensions of social interactions, e.g., pre-viewing (Hess et al., 2012), during viewing (Wohn & Na, 2011), and post-viewing (Giglietto & Selva, 2014) activity. 16
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Table 1. Descriptive Statistics of the Participants Gender Male Female Age 20-29 30-39 40-49 50-59 60-69 Education level High school graduate or less College student College graduate Advanced degree over BA Monthly income < $2,000 $2,000-$3,000 $3,000-$4,000 $4,000-$5,000 $5,000 < TV viewing (per a day) 1-2 hours 2-3 hours 3 hours < SNS use (1: rarely use - 7: use always)
Frequency
Percentage
216 226
48.9% 51.1%
137 140 99 55 11
31.0% 31.7% 22.4% 12.4% 2.5%
40 38 318 46
9.0% 8.6% 71.9% 10.4%
15 70 98 100 159
3.4% 15.8% 22.2% 22.6% 36.0%
70 15.8% 130 29.4% 242 54.8% Mean = 4.27 (SD = .69)
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Table 2. Principal Component Analysis Results for TSV Motivations Items
Components SCV EN
PT
To share my opinion on TV series with others
.844
.057
-.107
To interact with others
.834
-.004
.007
To chat with friends while watching TV
.758
-.110
.190
To share my real-time questions and thoughts on TV series with others during the TV series To have fun
.755
.123
-.124
.741
-.180
.317
To socialize with others
.693
.062
.106
To find out others’ views and thoughts
.666
.172
-.197
To share a sense of realism and suspense with others
.602
.249
.016
To watch TV together
.583
.289
.019
-.093
.851
.114
To boost the ratings of my favorite TV series
.028
.769
.184
To acquire new information about various TV series
.137
.746
-.074
To get information about programs I like
.222
.639
-.010
To help keep TV series I like on the air for many years to come
.366
.543
-.014
To relieve a sense of boredom
.131
.029
.875
-.023
.248
.848
8.05
1.16
1.52
50.28
7.22
9.49
.92
.86
.86
To put across my views or opinions to producers of TV series
To pass the time by watching TV Eigenvalue % Variance explained Cronbach’s α
Note: Principal component analysis with direct oblimin was used. SCV: Social co-viewing, EN: Engagement, PT: Passing time.
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Table 3. Principal Component Analysis Results for Social Interactions “On SNSs, I…”
Components Social sharing Issue surveillance
Post blogs or news articles about TV series or actors
.922
-.175
Post photos or videos about TV series or actors
.917
-.212
Send messages to people who have posted opinions on or information about TV series or actors Visit SNS pages of TV series or actors to post my opinions
.892
-.129
.871
-.114
Receive messages about TV series or actors
.753
.022
Send messages to friends or followers about TV series or actors Post opinions on TV series
.748
.007
.737
.113
Re-tweet or share others’ opinions or information about TV series or actors Follow accounts of TV series or actors
.728
.139
.715
.074
Receive suggestions for TV series from friends
.709
.154
Reply to or comment on others’ opinions on or information about TV series or actors Recommend TV series to friends
.692
.145
.640
.217
Give suggestions for TV series to friends
.624
.240
Receive recommendations for TV series from friends
.619
.271
Click “like” or “add to favorites” button on other people’s opinions on or information about TV series or actors View others’ photos or videos of TV series or actors
.525
.312
-.088
.880
Read articles or posts about TV series or actors through links
-.009
.836
View others’ opinions of TV series
.054
.762
Pay attention to articles, photos, or videos about TV series or actors
.202
.677
Read opinions on or information about TV series or actors through RT or sharing Eigenvalue % Variance explained Cronbach’s α Note. Principal component analysis with direct oblimin was used.
.310
.601
10.76 53.78 .95
1.93 9.67 .87
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Table 4. OLS Regressions Results for Predictors of Motivation and Social Interactions Motivation SCV Block 1. Controls (Intercept) Sociodemographic Age Gender (female = 0) Education level Income level Media Use TV viewing SNS use 2 R Block 2. Psychological Traits Innovativeness BAS BIS ΔR2 Block 3. Motivations SCV PT EN ΔR2 Total R2 F (df1, df2)
EN
Social interactions PT
SSB
ISB
3.71 (.63) *** 2.66 (.71) *** 3.73 (.82) ***
1.48 (.73) *
2.86 (.65) ***
-.01 (.01) **
.00 (.01)
-.01 (.01)
-.01 (.01)
.00 (.01)
-.04 (.1)
.08 (.12)
.15 (.13)
.04 (.12)
-.18 (.11) #
-.05 (.07) .04 (.04)
-.03 (.08) .03 (.04)
-.14 (.10) .01 (.05)
.03 (.08) .12 (.05) **
.11 (.08) .05 (.04)
.12 (.07) # .16 (.08) * .04 **
.18 (.08) * .11 (.09) .02 **
.18 (.09) * .04 (.10) .02 **
.28 (.08) *** .12 (.09) .06 ***
.22 (.07) ** .09 (.08) .05 **
.23 (.06) *** .40 (.07) *** .04 (.04) .24 ***
.36 (.07) *** .19 (.08) * .08 (.05) .18 ***
.13 (.08) # .30 (.10) ** .09 (.06) .08 ***
.25 (.07) *** .26 (.09) ** -.08 (.05) .11 ***
.18 (.06) ** .27 (.08) *** .05 (.05) .11 ***
.10 ***
-.01 (.07) .12 (.04) ** .43 (.06) *** .18 *** .35 ***
.19 (.07)** .06 (.04) .06 (.06) .06 *** .22***
.28 ***
.20 ***
18.22 (9, 432) 11.81 (9, 432) 5.13 (9,432)
19.24 (12, 429) 9.80 (12, 429)
Note. Coefficients are non-standardized OLS regression coefficients with standard error in parenthesis. SCV: Social co-viewing; EN: Engagement; PT: Passing time; SSB: Social sharing; ISB: Issue surveillance. N=442. # p < .1, * p < .05, ** p < .01, *** p < .001