Social television engagement: An examination of content, interpersonal, and medium relationships
First Monday

Social television engagement: An examination of content, interpersonal, and medium relationships by Jiyoung Cha



Abstract
This study aimed to identify factors that influence intention to engage in social TV. To that end, this study developed and tested a conceptual model that integrates content–, interpersonal–, and medium–relational factors. A survey of 275 college students in the United States suggests that individuals’ relationships with their contacts on an SNS, relationships with the SNS, affinity for viewing television programs, and preferences for certain types of television program genres predict engagement in social TV.

Contents

Introduction
Experience goods and word of mouth
Social TV and electronic word-of-mouth
Conceptual framework and hypothesis development
Method
Results
Discussion and conclusion

 


 

Introduction

Today, broadcasters and other television service providers encounter challenges and opportunities in the United States (U.S.). Prime-time ratings for the “big four” broadcast networks — ABC, CBS, NBC, and Fox — have significantly declined in recent years (Maglio, 2017). Subscriptions to cable and satellite television have also dropped (Biggs, 2017). Given these challenges, television networks and service providers use social networking sites (SNSs) to reboot the social elements of television viewing. Along with that trend, social television (social TV) has emerged.

Social TV refers to a computer-mediated system that allows individuals to share ideas and experiences regarding television programs with other people in different locations (Chorianopoulos and Lekakos, 2008). With the prevalent use of SNSs in the U.S., SNSs are widely used for social TV. Thus, social TV in this study is defined as the use of SNSs to share one’s experiences with television programs with others. Conversations about television programs via SNSs have been mounting in recent years. For instance, the number of tweets regarding the Super Bowl game during its telecast increased to 27.6 million in 2017 from 24.1 million in 2013 (Spangler, 2015; Perez, 2017). It is not uncommon to find industry reports attributing the breaking records of viewership of big events, such as the Super Bowl, the Olympics, and the Grammys to social TV (CBS News, 2015; Nielsen, 2017).

This social TV phenomenon is explained by the role of television content as a social tool. Not surprisingly, television content has been a tool for social interaction as something for people to talk about (Gorton, 2009). As television viewing has become more individualized and personalized in the multiplatform era, individuals’ conversations about and involvement with television content are readily mediated through various forms of media. Thus, social TV is a form of engagement with television content as mediated through SNSs. Television engagement is characterized by high involvement and absorption with television content (Nee and Dozier, 2017). Viewers have times when they are engaged with what they watch and other times when they watch simply to pass time. Viewers’ emotional responses to what they watch explain the difference between these moments (Gorton, 2009). Given that interactivity and sense of connectedness heighten engagement (Evans, 2008), SNSs are an inherently ideal medium to foster engagement with television content.

Industry studies have examined the relationship between social TV and television viewership. A study conducted by NM Incite and Nielsen found a positive correlation between social media buzz and television ratings (Subramanyam, 2011). When focusing on premiere episodes, Nielsen and SocialGuide found that an 8.5 percent increase in the amount of social buzz is associated with a one percent increase in TV program ratings for audiences ages 18 to 34 (Nielsen, 2013). Another industry report also demonstrated that the amount of tweets about television shows actually boosts the ratings for the shows (Walker, 2015).

The positive relationship between social TV and television ratings provides opportunities to television service providers. Despite the excitement, some news articles argued that social TV is dead (e.g., Roettgers, 2014). Their argument is based on the disappearance of some SNSs that specialized in social TV service. They include Tunerfish, GetGlue, Philo, IntoNow and Miso. It is worth noting that the majority of them were launched by start-ups, and start-ups come and go. It is true that some social TV services disappeared. However, it is still apparent that social TV per se is prevalent on other SNSs (Davies, 2017; Perez, 2017). Thus, the verdict on the death of social TV appears to be inappropriate.

The positive relationship between social TV and viewership of television programs has been described in industry reports (Nielsen, 2013; Nielsen, 2017; Subramanyam, 2011; Walker, 2015; Wilson, 2014). Now an important question to tackle is how to increase social TV engagement. Nevertheless, both scholarly and industry research investigating this issue are still scarce. Thus, this study aims to understand factors that influence viewers’ intention to engage in social TV. To that end, this study proposes a conceptual framework grounded in the relational paradigm of exchange, and empirically tests the framework.

 

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Experience goods and word of mouth

In economics, television programs are classified as experience goods. An experience good is a product or service whose overall quality is difficult to assess before the actual experience with or consumption of the product (Nelson, 1970). Viewers are uncertain of the overall quality of television programs until they actually watch them. Word of mouth (WOM) plays a critical role in consumer choice and post-purchase perceptions of experience goods, due to the uncertainty associated with experience goods (Murray, 1991). WOM is “an exchange of comments, thoughts, and ideas among two or more individuals in which none of the individuals represent a marketing source” [1]. WOM is prevalent for experience goods like television programs and movies (Liu, 2006).

WOM influences consumer attitudes and behavioral intentions (Chatterjee, 2001; Chevalier and Mayzlin, 2006). WOM also affects consumer choice and post-purchase perceptions (Bone, 1995; Henning-Thurau and Walsh, 2004). Comparing different types of communication channels, WOM has a greater impact on consumer attitudes and behaviors than other communication channels, e.g., editorial recommendations and advertising, including radio and print advertising (Goldsmith and Horowitz, 2006; Smith, et al., 2005; Trusov, et al., 2009).

Despite the prolific research regarding WOM on consumer behavior, few researchers have investigated how WOM influences choice and performance of television programs. In the context of movies, which are considered experience and cultural goods like television programs, interpersonal communication, such as WOM, has a greater influence on movie choice than does mass media (Faber and O’Guinn, 1984). Interpersonal sources drew more attention to the most recent films that college students watched than did mass media (Austin, 1981). WOM complements other marketing methods in marketing movies, and the amount of WOM prior to the release of films influences box office revenue (Liu, 2006). Likewise, a reason why WOM is a strong persuasive communication channel is because consumers tend to trust the information from the people they know more than the information from marketers (Goldsmith and Horowitz, 2006). Thus, WOM is considered a more reliable communication channel than other marketing channels (Gruen, et al., 2006).

 

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Social TV and electronic word-of-mouth

Since the emergence of the Internet, a new form of WOM called electronic word of mouth (eWOM) has become prevalent. eWOM refers to a potential, actual, or former customer’s comment about a product or company that is made widely available to people and institutions via the Internet (Hennig-Thurau, et al., 2004). Like WOM, eWOM influences a product choice, purchase, and use (Chevalier and Mayzlin, 2006; Senecal and Nantel, 2004). The effect of eWOM on purchase decisions is greater for experience goods than for search goods (Park and Lee, 2009). Individuals’ comments when expressing their ideas and sharing information on SNSs is a form of eWOM (Hennig-Thurau, et al., 2004). Thus, social TV is eWOM through SNSs whose subject matter focuses on television programs. However, social TV has qualities and characteristics different from eWOM through non-SNSs.

eWOM through SNSs and eWOM through non-SNSs differ. First, eWOM via SNSs would increase the potential for individuals to receive information regarding a product from known sources (e.g., friends, family, and acquaintances) or to send information to known recipients (i.e., the people one knows or the people one connects with on SNSs). By contrast, people who seek information about a product via non-SNSs typically access the information created by unknown sources. They do not have relationships with the creators of the message. People who share information about a product via non-SNSs also disseminate the information to unknown recipients. The source of the information is a key to influencing the degree to which people trust the information they receive, and people tend to trust the information from people they know (Goldsmith and Horowitz, 2006). The product information from a person one knows is increased in value (Phelps, et al., 2004). The recipient is more likely to pass on the information to other people than if he or she receives it from an unfamiliar interpersonal source or a commercial source (Chiu, et al., 2007; Phelps, et al., 2004). Given that social TV is an eWOM through SNSs, social TV would create a greater amount of buzz about television programs.

Second, eWOM through SNSs enables the interaction between the sender and receiver of information to be easier and more feasible, because the sender and receiver are linked to one another on the SNS. In the context of eWOM through non-SNSs, the interaction between the sender and receiver of information is constrained, because they do not know each other and they are not linked on the Internet. Conversations about products or services are not necessarily synchronous in both the eWOM through SNSs and eWOM through non-SNSs. However, eWOM through SNSs is more likely to increase possibilities of prompt interaction between the sender and receiver of the information than eWOM via non-SNS, because SNSs directly link the sender and receiver of the information. The relatively short time lag of the interaction between the sender and receiver makes eWOM through SNSs more efficient and effective than eWOM via non-SNSs in increasing the amount of buzz about products/services.

Third, the recipient of information can be passive in the context of eWOM through SNSs, whereas the recipient of information should be active in the context of eWOM through non-SNSs. Even without actively searching for information about products/services, SNS users are still exposed to a great amount of information that is provided by their contacts on the SNSs. In the context of eWOM via non-SNSs, individuals, by contrast, would likely have to be actively searching for the information about products/services. Thus, social TV, which is a form of eWOM through SNSs, allows a message to reach wider potential audiences than eWOM via non-SNSs, such as online review sites of television programs.

 

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Conceptual framework and hypothesis development

Given the aforementioned advantages of social TV, the present study aims to examine factors that influence social TV engagement through the lens of the relational paradigm of exchange. The relational exchange paradigm views exchange as a continuous process focused on the creation of value through relationships with various parties (Hankinson, 2004; Payne, et al., 2001). Social TV encompasses three forms of relationships: 1) content relationship; 2) interpersonal relationship; and 3) medium relationship.

The first form of relationship is the content relationship. The content relationship is defined as an individual’s relationship and connection with the subject matter (i.e., television content in this study). Product involvement is one of the motives for WOM behavior (Dichter, 1966; Sundaram, et al., 1998). The key is that individuals’ cognitive, emotional, and behavioral involvement with products differ across product types (Park and Lee, 2009). The level of engagement in exchange of communication and information depends on the subject matter. Since the subject matter of social TV focuses on television content, an individual’s relationship with television content is the integral part of social TV. This study recognizes the characteristics of television programs as experience and cultural goods and thus theorizes that intention to engage in social TV serves as a function of individuals’ relationship with television content.

The second form of relationship that is grounded in social TV is the interpersonal relationship. An interpersonal relationship refers to one’s relationship with other people with whom she exchanges messages on the SNS. The link between communicators exists independently of specific content; thus the linkage also influences the process of WOM (Knoke and Kuklinski, 1982; Brown and Reingen, 1987). Any WOM communication occurs within an individual’s relationship with others (Bristor, 1990; Brown, et al., 2007; Money, et al., 1998). Social TV is no exception. Given that social TV is a form of eWOM, one’s relationship with other individuals on the SNS is an essential element of social TV. Interpersonal relationships are not merely containers for communication; they influence how people perceive the world and establish world views (Duck and Pond, 1989; Carl and Duck, 2004). Thus, it is crucial to examine how one’s relationship with other contacts on the SNS affects social TV engagement.

The third form of relationship that may trigger social TV engagement is the medium relationship. The medium relationship refers to one’s connection with the medium that is used for communication of the message. In the context of social TV, a message contains an individual’s feelings, ideas, opinions, information about television programs. The medium used to express the message is an SNS. Brown, et al. (2007) found that eWOM is different from WOM in that the Web site used for communication acts as a social proxy for a communicator’s relationships with others. Thus, the credibility of eWOM also depends on the individual’s relationship with the Web site. Because social TV is an eWOM about television programs mediated through SNSs, an individual’s relationship with the SNS would influence the degree to which he engages in social TV.

Grounded in the relational paradigm of exchange, the present study proposes that intention to engage in social TV is a function of content, interpersonal and medium relationship factors. Figure 1 illustrates the conceptual framework. Specifically, the content relationship factors are represented by affinity for viewing television programs and preferences for television program genres. The interpersonal relationships are represented by relational trust, tie strength and homophily. The medium relationship is represented by the frequency of using an SNS.

 

Conceptual framework for predicting intention to engage in social TV
 
Figure 1: Conceptual framework for predicting intention to engage in social TV.

 

Content relationship

Genre preferences. Industry studies have reported that social TV boosts ratings of the television programs mentioned in social TV conversations. NM Incite and Nielsen found a positive correlation between social TV and television ratings (Subramanyam, 2011). A more recent study found that tweets about television programs increased viewership, and it emphasized the causation between social TV and television viewership (Wilson, 2014; Walker, 2015). Some prior studies address possibilities that television genres influence the level of social TV engagement (e.g., Cha, 2016; Steel, 2011). Deery (2003) found that certain genres, such as reality shows, leverage the linkage between the Web and television. Steel (2011) suggested that television shows with potentially controversial storylines might be more likely to draw more buzz as a matter of course.

It appears that one reason why people might share watching certain genres more than other genres is because certain genres induce more emotional involvement than others. Reality television allows audiences to experience the “real” world through observation of others’ trials and tribulations, and thus reality television makes audiences experience not only cognitive involvement but also emotional involvement with the programs (Nabi, et al., 2006). Lull (1990) also suggested that people-centered content triggers audiences to engage in conversations. Stone (1981) noted that sports spectacles are “conversation-pieces, and conversations about them before, during, and after the event bring people together in an emotional rapport” [2].

Although a few studies explored the possible link between television program genres and social TV engagement, they focused on the question of why people tend to talk more about certain genres, instead of what program types are more common than others for social TV conversations. This study takes a quantitative approach in order to gauge the relationship between television genre preferences and social TV engagement.

RQ1: How are television program genre preferences related to intention to engage in social TV?

Affinity for viewing television programs. Rubin (1981) suggested that television attitudes consist of affinity with the medium and perceived content reality. Rubin (2002) found that more ritualistic consumption of a medium (i.e., habitual and less active viewers) is linked to an affinity with the medium, whereas more instrumental consumption of a medium is related to an affinity with the content selected. More instrumental use of a medium is related to greater activity levels and relatively greater affinity with the content than with the medium (Rubin, 1983). As newer platforms for video consumption emerged, affinity with the medium should be separated from affinity with the content.

Prior studies found a positive relationship between affinity for certain content and activities relevant to the television programs. Specifically, affinity with political content on television is a positive predictor of participation in political activities (Earnheardt, 2013). Affinity with soap operas is related to post-viewing program discussion (Rubin and Perse, 1987). Dependence on television news (Levy, 1979; Houlberg, 1984) and television shopping programs (Grant, et al., 1991) are linked to parasocial interaction (i.e., a sense of affective interpersonal involvement with media personalities) whose common form is “imagining being part of a favorite program’s social world” [3]. Affinity for reality TV programs is also positively related to parasocial interaction (Ebersole and Woods, 2007). Given that prior studies found a relationship between affinity for content and relevant social activities (e.g., Earnheardt, 2013; Grant, et al., 1991), the following hypothesis is proposed:

H1: Affinity for viewing television programs is positively related to intention to engage in social TV.

Interpersonal relationship

Relational trust. Trust refers to “a willingness to rely on an exchange partner in whom one has confidence” [4]. Trust affects the relationship, interaction, and behaviors of two parties (e.g., Izquierdo and Cillán, 2004; McAllister, 1995). In communication processes, source trust or source credibility received particular attention. Source trust is “the resultant value of: (1) the extent to which a communicator is perceived to be a source of a valid assertions, and (2) the degree of confidence in the communicator’s intent to communicate the assertions he/she considers most valid” [5]. Prior studies investigated how the level of perceived trust held by a message recipient regarding the individual message-sender or the advertiser influences the diffusion of a message in various contexts (e.g., Cho, et al., 2014; Louie and Obermiller, 2002; Soh, et al., 2009). Cho, et al. (2014) found that the recipient of a viral e-mail is more likely to pay attention to the e-mail if the recipient trusts the sender.

In the context of social TV, individuals can seamlessly switch their role from the sender of a message to the receiver, and vice versa. That is, anyone can post a message about a television show on an SNS as a message sender. At the same time, they can also engage in social TV conversations initiated by someone else. Thus, relational trust among the message exchangers is more relevant than source trust in predicting intention to engage in social TV. Relational trust refers to the degree to which the sender and receiver of a message believe in each other’s trustworthiness. Relational trust affects a truster’s positive expectations about an interaction with the trustee (Lewicki and Bunker, 1996; Rousseau, et al., 1998). Relational trust also positively influences the level of information exchange in online environments (Javenpaa, et al., 1988; Ridings, et al., 2002; Chu and Kim, 2011).

Over the past years, established and new SNSs have emerged as social TV service providers. Some of them are general SNSs, such as Twitter and Facebook, and they were not specifically designed for social TV. Others specialize in social TV; they were brought to the marketplace to stimulate social TV conversations. Depending on which SNS an individual uses for social TV engagement, the individual might interact on the SNS with other people that they trust or trust less. Based on the positive effect of trust on information exchange online (Javenpaa, et al., 1988; Ridings, et al., 2002; Chu and Kim, 2011), the following hypothesis is proposed:

H2: Relational trust is positively related to intention to engage in social TV.

Tie strength. Tie strength has been found to be an important factor that determines the effect of WOM communications (Brown and Reingen, 1987). Tie strength refers to “the potency of the bond between members of a network” [6]. Tie strength is “a multidimentional construct that represents the strength of the dyadic interpersonal relationships in the context of social networks” [7]. Tie strength influences the ways, means, and expression of communication between the two parties (Haythornthwaite, 2002; Steffes and Burgee, 2009). Rogers (1995) maintained that the information from strong-tie sources is considered more credible and trustworthy than the one from weak-tie sources.

Research shows that tie strength positively affects the frequency of communication and the amount of information exchanged between parties. Brown and Reingen (1987) found that people in a strong tie relationship communicate more often and exchange more information than ones in a weak tie relationship. In a viral marketing context, tie strength influences the decision of the recipient to open the e-mail he received (De Bruyn and Lilien, 2008) and the likelihood that he will forward the message to others (Chiu, et al., 2007). The tie strength between the sender and receiver of the information also has a positive relationship with the influence the sender’s information will have on the receiver’s purchase decision (Bansal and Voyer, 2000). Chu and Kim (2011) found that tie-strength with contacts on an SNS is positively related to not only opinion-seeking but also passing on the information of a product via SNSs. Thus, the present study proposes the following hypothesis:

H3: Tie strength is positively related to intention to engage in social TV.

Homophily. Homophily refers to “the degree to which pairs of individuals who interact are similar with respect to certain attributes, such as beliefs, values, education, social status, etc.” [8]. People are more likely to trust those who are similar to them than the ones who are dissimilar (Bashein and Markus, 1997). People tend to exchange messages or interact most frequently if they feel similar, that is, homophilous (Byrne, 1971; Rogers and Bhowmik, 1970). Empirical studies found that people are more likely to interact if they perceive each other to be similar in terms of age, ethnicity, education level and status (Ibarra, 1992; Leenders, 1996; Mollica, et al., 2003).

The search and filter functions of SNSs enable individuals to easily locate and connect with others who are similar to them. Prior studies examined homophily between SNS users and their contacts. Myspace users were found to be similar with their Myspace contacts in terms of ethnicity, age, religion, sexual orientation, country, marital status and attitude toward children (Thelwall, 2009). Twitter users tend to share similar interests with their contacts (Weng, et al., 2010). A certain level of homophily was found among people who follow each other’s Twitter accounts in terms of location and popularity (i.e., the number of followers) (Kwak, et al., 2010).

In off-line settings, numerous studies demonstrated that homophily increases social interaction (Ibarra, 1992; Leenders, 1996; Mollica, et al., 2003). By contrast, Chu and Kim (2011) found that homophily has a negative relationship with SNS users’ opinion-seeking and opinion-passing of new products through SNSs. A possible explanation for this may be that Chu and Kim’s (2011) study focused on general consumer products without specifying product categories. Although users may find a great degree of homophily with their contacts on an SNS, they may not want to seek or forward information about new consumer products with those contacts.

Given that the present study focuses on television programs, which are cultural goods, the more individuals perceive their contacts on an SNS as being similar, the more likely they are to engage in social TV. Indeed, television programs have been playing a substantial role in constructing and maintaining interpersonal relations (Lull, 1990). A few studies found that SNS users tend to be similar to their contacts on an SNS at the aggregate level (e.g., Kwak, et al., 2010; Thelwall, 2009; Weng, et al., 2010), but the degree of homophily may differ at the individual level. Given the positive relationship between homophily and interaction (Ibarra, 1992; Leenders, 1996; McPherson, et al., 2001; Mollica, et al., 2003), the following hypothesis is proposed:

H4: Homophily is positively related to intention to engage in social TV.

Medium relationship

Frequency of using the SNS. The notion of social TV is built upon the use of SNSs. Thus, use of SNSs is essential to social TV behaviors, and an individual’s relationship with an SNS might influence social TV engagement. In the context of eWOM, Brown, et al. (2007) suggested that an individual’s relationship to the Web site on which they communicate influences the flow of eWOM conversations. Hence, an individual’s relationship with an SNS may affect his intention to engage in social TV.

Prior studies showed that people who are highly involved with SNSs spend more time, effort, and energy using SNSs (Chi, 2011; Östman, 2012). Alhidari, et al. (2015) found that individuals highly involved with SNSs are more likely to engage in eWOM. People who seek interpersonal utility use SNSs more frequently (Cha, 2010; Smock, et al., 2011). Thus, people who use SNSs often are likely to find various sources for social interaction. As experience and cultural goods, television programs have been a source for social interaction. Television viewing fulfills social interaction needs (Stafford, et al., 2004). Frequent SNS users are also more likely to be exposed to conversations about television programs on SNSs. Thus, being a frequent SNS user would increase the likelihood of that user engaging in social TV.

H5: Frequent SNS use is positively related to intention to engage in social TV.

 

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Method

Data collection

A survey was employed to test the hypotheses and answer the research question. College students who enrolled in undergraduate courses within a public university participated in the online survey. The students had various majors, and participation was voluntary. A total of 275 responses was used for data analysis. The mean age of the survey participants was 21.79 (SD = 4.10). The participants were composed of 41.1 percent male (coded 0) and 58.2 percent female (coded 1). With respect to ethnicity, 10.9 percent identified themselves as African Americans, 20.4 percent as Asians, 30.9 percent as Caucasians, 24.7 percent as Hispanics, and 12.0 percent as other.

College students were deemed appropriate because they are also a lucrative age group for television networks and advertisers. Social media is an essential part of college students’ lives and is widely used among college students (Modo Labs Team, 2016). Research conducted by Crux Research indicated that eight out of 10 college students reported using a second screen at least a few times a week while watching television (eMarketer, 2013). The same research also illustrated that the most popular activity students engaged in while watching television was using Facebook or Twitter (eMarketer, 2013).

Measures

Measures for the present study were adapted from prior studies, as explained below. A principal component factor analysis with varimax rotation was first performed to assess construct validity of the measures. This study employed widely-accepted rules of minimum eigenvalue of 1.0 and at least 2 (60/40) loadings per factor to extract factors (e.g., Sun, et al., 2008). The factor analysis yielded five factors, accounting for 80.54 percent of the total variance and confirming both convergent and discriminant validity of the measures (see Table 1).

 

Table 1: Principal component factor analysis.
ConstructsSocial TV engagementAffinityTrustTie strengthHomophily
Intention to engage in social TV 
I would comment about television programs on an SNS(s) in the future..90.14.08.14.60
I would recommend television programs on an SNS(s)..87.18.13.11.10
I would post my thoughts about television programs on a social networking site(s)..89.19.17.13.10
I would engage in conversations regarding television programs on a social networking site(s)..90.13.10.11.51
I would share what television programs I watch on an SNS(s).86.24.09.09.12
Affinity for viewing TV programs 
I would rather watch television programs than do anything else..17.81.05-.04.09
I would feel lost without watching television programs..15.87.07.03-.01
I could easily do without watching television programs for several days..02.64-.05.14-.09
Watching television programs is one of the more important things I do each day..22.83.10.02.04
Watching television programs is very important in my life..24.81.14-.05.03
Relational trust 
I trust most of my contacts on the social networking site..13.06.88.11.24
I have confidence in my contacts on the social networking site..19.11.8922.23
I can believe in my contacts on the social networking site..16.11.89.20.20
Tie strength  
Approximately how frequently do you communicate with your contacts on the social networking site?.19.01.09.84.16
Overall, how important do you feel about your contacts on the social networking site?.16.03.22.85.22
Overall how close do you feel to your contacts on the social networking site?.12.08.22.82.31
Homophily 
In general, my contacts on the social networking site are similar to me..14.03.22.29.76
In general, my contacts on the social networking site think like me..10-.01.14.14.87
In general, my contacts on the social networking site behave like me..05.02.14.14.89
In general, my contacts on the social networking site are like me..09-.01.18.15.91
Eigenvalue4.263.393.322.692.45
Variance explained21.3216.9316.6213.4512.23
Cronbach’s alpha.96.87.95.89.92

 

Intention to engage in social TV. The measure for intention to engage in social TV was adapted from Godlewski and Perse (2010). The respondents were asked to indicate the level of agreement with five statements, including: “I would comment about television programs on an SNS(s) in the future” and “I would share what television programs I watch on an SNS(s)” on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The measure was reliable (Cronbach’s α = .96, M = 4.30, SD = 1.78).

Content relationship. To measure affinity for viewing television programs, five items were adapted from Rubin (1981) and Ebersole and Woods (2007). Respondents were asked to indicate how much they agree with statements such as: “I would feel lost without watching television programs” and “Watching television programs is very important in my life” on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The measure was reliable (Cronbach’s α = .87, M = 2.74, SD = 1.43). For television genre preferences, respondents were asked to indicate how much they like a specific television program genre on a 7-point scale (1 = dislike very much, 7 = like very much). The study focused on 10 television program genres (i.e., action/adventure, comedy, drama, talk/interviews, variety/music, news/information, game shows, movies, reality shows and sports). The genre categories were based on Hall (2005), Potts, et al. (1996) and Preston and Clair (1994). The descriptive statistics indicate that the respondents like movies (M = 6.45, SD = .92) and comedy (M = 6.43, SD = .97) most, whereas they like reality television (M = 3.66, SD = 1.99) least.

Interpersonal relationship. After gauging respondents’ intention to engage in social TV, the respondents were asked to choose one SNS they are most likely to use to share their experiences with television programs. Then, they were asked to answer the questions about relational trust with their contacts on the SNS, tie strength with the contacts on the SNS, homophily with the contacts on the SNS and frequency of using the SNS.

To measure relational trust with the contacts on the SNS, three items were adapted from Lin (2006), Mortenson (2009), Smith, et al. (2005), and Chu and Kim (2011). Respondents were asked to indicate their level of agreement with statements such as: “I trust most of my contacts on the SNS” and “I have confidence in my contacts on the SNS” on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The measure was reliable (Cronbach’s α = .95, M = 3.68, SD = 1.28). Tie-strength with the contacts on the SNS was measured using three items adapted from prior studies (i.e., Brown and Reingen, 1987; Norman and Russell, 2006; Reingen and Kernan, 1986, Chu and Kim, 2011) on a 7-point-semantic differential scale. The items included: “Overall, how important do you feel about your contacts on the SNS?” (1 = not at all important, 7 = very important) and “Overall, how close do you feel to your contacts on the SNS?” (1 = not at all close, 7 = very close). The measurement items were reliable (Cronbach’s α = .89, M = 4.31, SD = 1.35). Homophily measured an individual’s perceived similarity with his/her contacts on the SNS. Four items were adapted from McCroskey, et al. (1975) and Chu and Kim (2011). Respondents were asked to complete the sentence: “In general, my contacts on the SNS ...” with choices from the following pairs of statements: are different from me/similar to me; don’t think like me/think like me; don’t behave like me/behave like me; are unlike me/like me on a seven point semantic differential scale (1 = are different from me, 7 = are similar to me). The measurement items were reliable (Cronbach’s α = .92, M = 4.47, SD = 1.22).

Medium relationship. Frequency of using the SNS was measured using two items that ask how many days respondents use the SNS during a typical week, and the frequency of using the SNS on a 7-point scale (1= never, 7 = all the time). The measure was reliable (Cronbach’s α = .82, M = 5.97, SD = 1.43).

Statistical analysis

Ordinary Least Square (OLS) regression was conducted to answer the research question and test the hypotheses. The model accounted for 35.6 percent of the total variance. The model’s variance inflation factor (VIF) ranged from 1.22 to 1.76, which indicates no multicollinearity problem.

 

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Results

RQ1 addressed how television program genre preferences are related to intention to engage in social TV. Two-tailed Pearson correlations were first conducted to examine the bivariate relationship between preferences for each television program genre and intention to engage in social TV. The results from Pearson correlations show that action (r = .13, p < .05), comedy (r = .22, p < .001), drama (r = .36, p < .001), reality shows (r = .17, p < .01), game shows (r = .13, p < .05), movies (r = .21, p < .01), and music (r = .22, p < .001) are positively correlated with intention to engage in social TV (see Table 2). Preferences for talk/interviews, news and sports are not statistically significantly correlated with intention to engage in social TV.

 

Table 2: Pearson correlations between intention to engage in social TV and genre preferences.
Note: *p < .05, **p < .01, ***p < .001 (two-tailed).
 Social TV engagementActionComedyDramaTalk/
interviews
Reality showsSportsGame showsMoviesMusicNews
Social TV engagement1          
Action/adventure.13*1         
Comedy.22***.23***1        
Drama.36***.21***.27***1       
Talk/interviews.11-.06.11.16**1      
Reality shows.17**-.00.05.19**.35***1     
Sports-.02.17**.06.02.08.101    
Game shows.13*.25***.13*.16**.18**.26***.35**1   
Movies.21**.29***.37***.38***.16**.08.140*.121  
Music/variety.22***.14*.22***.20**.22***.22***.15*.22***.33***1 
News/information.06.06.04.15*.45***.10.12.04.14*.21***1

 

Genre preferences may not be the only predictors of social TV engagement. Thus, an OLS regression, with genre preferences and other independent variables, was further carried out. Table 3 shows the results of the OLS regression. The results from OLS regression indicate that preferences for drama (β = .20, p < .01) and music television shows (β = .13, p < .05) are positively related to intention to engage in social TV. Preferences for the other television program genres do not have statistically significant relationships with intention to engage in social TV after controlling for the other independent variables.

H1 proposed a positive relationship between affinity for viewing television programs and intention to engage in social TV, and was supported (β = .23, p < .001). H2 and H3 proposed that relational trust and tie strength with the contacts on the SNS are positively related to intention to engage in social TV. As seen in Table 3, the results of the OLS regression indicate that both relational trust (β =.13, p < .05) and tie strength (β =.14, p < .05) are positively related to intention to engage in social TV. Therefore, H2 and H3 are supported. H4 postulated a positive relationship between homophily with the contacts on the SNS and intention to engage in social TV. The OLS regression found no statistically significant relationship between the two variables. Thus, H4 was not supported. H5 addressed a positive relationship between frequency of using the SNS and intention to engage in social TV. The OLS regression confirms that frequency of using the SNS (β = .20, p < .01) has a statistically significant positive relationship with intention to engage in social TV. Thus, H5 was supported.

 

Table 3: OLS regression for intention to engage in social TV.
Note: *p < .05, **p < .01, ***p < .001 (two-tailed).
 BSEBeta
Constant3.449.919 
Affinity for viewing television programs.289.068.231***
Preference for action/adventure.023.075.017
Preference for comedy.080.105.044
Preference for drama.277.079.204**
Preference for talk/interviews.004.067.004
Preference for reality shows-.024.051-.027
Preference for sports.005.046.006
Preference for game shows.013.065.012
Preference for movies.114.119.058
Preference for music/variety.153.069.125*
Preference for news/information-.014.067-.012
Relational trust on contacts on SNS.187.087.134*
Tie strength with contacts on SNS.189.087.143*
Homophily.062.068.042
Frequency of SNS use.262.079.196**
F (15,243)10.521***  
R2.394  
Adjusted R2.356  

 

 

++++++++++

Discussion and conclusion

Possible contributions of this study are threefold. First, this study proposes a model that predicts intention to engage in social TV. Scarce research regarding social TV exists, and prior studies analyzed social TV engagement per se or social TV’s influence on advertising effectiveness (e.g., Bellman, et al., 2017). The present study progressed one step further by investigating how social TV engagement is boosted. Second, this study conceptualizes social TV as a form of eWOM; it recognizes possible impacts of individuals’ relationships with the product and the communication medium on the degree to which they are likely to engage in eWOM. Third, this study offers guidelines on how broadcasters and social media firms can leverage social TV and possible explanations for why some social TV services failed.

Results indicate that one’s relationship with television content, the SNS and contacts on the SNS are all important in predicting intention to engage in social TV. Specifically, affinity for viewing television programs, the preference for the drama genre, and the frequency of using the SNS are the three most important factors affecting intention to engage in social TV. The findings demonstrate the crucial impacts of an individual’s relationship with the product (television content in this study) and the communication medium (SNS in this study) on eWOM engagement. Prior studies disregarded an individual’s relationship with the product and the SNS, tending to focus on an individual’s relationship with their SNS contacts in predicting intention to engage in eWOM through an SNS (e.g., Chu and Kim, 2011; Luo and Zhong, 2015). The present study suggests that future eWOM studies should further examine how the relationship with the product and the communication medium come into play in various product and communication media settings.

The level of social TV engagement is not universal across various genres. The more that individuals prefer drama and music/variety, the more likely they are to engage in social TV. Individuals’ tendency of sharing music per se or opinions and thoughts about music/variety television programs with others on the Internet in general appears to be dominant in the social TV scene. Drama requires that the viewers pay a lot of attention to follow the plot, and thus induces viewers to become emotionally involved with and attached to the content.

The preference for drama is positively related to intention to engage in social TV. This result corroborates the findings of Godlewski and Perse (2010), who reported that greater attention to the television program and greater emotional involvement increase viewers’ online activities. In the context of soap operas, Rubin and Perse (1987) found that the amount of viewing attention to the content and characters, and perceived realism of content are correlated with parasocial interaction. Expression of emotions such as joy, anger, fear and sadness is a primary social TV activity on Twitter (Buschow, et al., 2014). The high level of viewing attention and the high perception of realism in dramas make it more likely that people will express emotions and share information about dramas via SNSs. Prior studies found a positive relationship between the amount of social TV conversations and the viewership of the corresponding television programs (Chen, 2015; Nielsen, 2013; Schweber, 2016; Subramanyam, 2011; Walker, 2015). When their findings are combined with the results from the present study, it seems apparent that drama and music/variety television programs are more likely to benefit from social TV in terms of viewership.

People who have greater affinity for viewing television programs are more likely to engage in social TV. Focusing on reality shows, Ebersole and Woods (2007) found that affinity for reality shows is positively related to parasocial interaction. The present study corroborates the important role of affinity with content in parasocial activity, and further demonstrates that the effect of affinity on parasocial activity is not bound to reality shows. Note that the present study focused on affinity for viewing television programs instead of affinity with television. Given that television is not the only platform that allows people to watch television programs, this study reveals that affinity with content (i.e., television programs) is a determinant of individuals’ intention to engage in conversations about television programs on SNSs. In a multiplatform era, it would be important to distinguish between affinity with content and affinity with medium in examining the role of affinity in audience behavior.

Tie strength with the contacts on an SNS is a salient predictor of social TV engagement. The stronger the ties an SNS user perceives regarding his/her contacts on the SNS, the more likely she is to engage in social TV. In the context of eWOM via SNSs, Chu and Kim (2011) examined three separate eWOM behaviors — opinion-seeking, opinion-giving, and opinion-passing of products — and found that tie strength does not predict consumers’ intention to provide information about products via SNSs, although it predicts opinion-seeking and opinion-passing. The present study focused on television programs, whereas Chu and Kim (2011) did not designate a particular product type in investigating engagement in eWOM through SNSs. Television programs differ from general consumer products in that television programs are experience and cultural products. These characteristics of television programs appear to explain why individuals are more likely to engage in social TV if they feel close to their contacts on an SNS. Future studies can further examine how the nature of a product moderates the relationship between tie strength and eWOM engagement via social media.

Along with tie strength, an individual’s relational trust with contacts on an SNS positively predicts intention to engage in social TV. The role of trust has been examined in the context of viral marketing. Those studies tended to focus on how the degree to which the receiver of product information trusts the message sender influences the viral advertising effects, including the receiver’s attention to the product information and the receiver’s attitudes toward the product information (e.g., Cho, et al., 2014). The substantial role of relational trust in social TV engagement contributes to the existing literature in that the present study examined how the degree to which the sender of a message about television programs trusts the possible receivers of the information affects the likelihood of engaging in social TV. Although prior studies tended to emphasize the receiver’s trust toward the sender, the present study suggests that the sender’s trust toward the receiver also increases social interaction in a situation where an individual can easily exchange roles from a sender to a receiver, and vice versa. The finding also supports prior studies that found trust as a positive predictor of individuals’ intention to share and exchange information with others via virtual communities (Chu and Kim, 2011; Ridings, et al., 2002).

The construct of trust is an important factor affecting the relationship and behaviors of two parties in various disciplines (Lewicki and Bunker, 1996; McAllister, 1995), but trust was often not distinguished from tie strength. However, trust and tie strengths are not interchangeable: it is possible for one to trust another individual while having a weak tie with them (Levin and Cross, 2004). Given the backdrop, the present study shows that the extent to which social media users trust their contacts on SNSs influences the level of intention to engage in social TV. Luhmann (1988) maintained that trust might not influence the recipient’s decision-making processes if the decision involves no risk. In contrast, the findings from the present study demonstrate that relational trust affects engagement in social TV, although exchanging information and feelings about television programs via SNSs has minimal or no risks.

Frequency of using the SNS is another important predictor of social TV engagement. As mentioned previously, relational trust and tie strength with the contacts on the SNS also boost the likelihood of social TV engagement. These combined results suggest that individuals are more likely to engage in social TV on the SNS that they use often and on which they have close and trusted contacts. Some social TV sites or applications (e.g., Miso, GetGlue, IntoNow, Tunerfish) failed. Those that disappeared have a common trait: they were not based on one’s existing networks. The sites encouraged their users to share their experiences regarding television programs with strangers. They treated social TV as being similar to an eWOM through non-SNSs. Thus, they disregarded how their users’ relationships with the sites and other users come into play in driving up social TV engagement. People are more likely to engage in social TV to strengthen bonding with their existing networks, as opposed to networking with new people (Cha, 2016). The findings from the present study illustrate that people want to use social TV as an interpersonal utility with close and trusted people, not as a one-way means to broadcast their television experiences to strangers. Social TV services should be built based on users’ existing social networks, including friends, family members, colleagues, etc.

This study was one of the first studies that investigated determinants of social TV engagement, and provides interesting insights. However, its limitations should be noted, with possible future research directions. First, the use of a convenience sample of college students limits the generalizability of the findings. College students in the U.S. are one of the groups that often uses SNSs while watching television (eMarketer, 2013), but the data for the present study were collected at a single university. Thus, it would be valuable to replicate this study at universities in different settings. Second, this study employed a survey to gauge social TV engagement and focused on intention rather than actual behavior. Content analysis of actual social TV conversations would deepen the understanding of the relationship between genre types and social TV engagement. Third, the conceptual model proposed by this study focused on developing an economic model that predicts social TV engagement. The building of a comprehensive model would provide additional explanations about audience behavior and implications for television and social media service providers. Thus, future research should encompass more constructs to explain social TV engagement. End of article

 

About the author

Jiyoung Cha, Ph.D., is an associate professor in the Department of the Broadcast and Electronic Communication Arts at San Francisco State University. Her research aims to understand the competitive dynamics of the media marketplace, how new media change audiences’ media consumption patterns and the business principles of media firms, and why audiences adopt or reject new communication technologies. Her research has appeared in peer-reviewed journals, including the Journal of Media Economics, International Journal on Media Management, Journalism and Mass Communication Quarterly, Telematics and Informatics, First Monday, and Journal of Advertising Research among others. She earned her Ph.D. in mass communication with a minor in marketing from the University of Florida.
E-mail: jycha [at] sfsu [dot] edu

 

Notes

1. Bone, 1992, p. 579.

2. Stone, 1981, p. 222.

3. Rubin, et al., 1985, pp. 156–157.

4. Moorman, et al., 1993, p. 82.

5. Hovland, et al., 1953, p. 21.

6. Mittal, et al., 2008, p. 196.

7. Money, et al., 1998, p. 79.

8. Rogers and Bhowmik, 1970, p. 525.

 

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Editorial history

Received 21 March 2018; revised 30 November 2018; accepted 3 December 2018.


Copyright © 2019, Jiyoung Cha. All Rights Reserved.

Social television engagement: An examination of content, interpersonal, and medium relationships
by Jiyoung Cha.
First Monday, Volume 24, Number 1 - 7 January 2019
https://ojphi.org/ojs/index.php/fm/article/view/8548/7707
doi: http://dx.doi.org/10.5210/fm.v24i1.8548





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