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Time displacement effect of online video services on other media in South Korea
Telematics and Informatics 33 (2016) 247–255
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Telematics and Informatics journal homepage: www.elsevier.com/locate/tele
Time displacement effect of online video services on other media in South Korea Seung Yeop Lee a,b, Sang Woo Lee a,⇑, Changwan Kim c a
Graduate School of Information, Yonsei University, Seoul, Republic of Korea Korea Communications Agency (KCA), Naju, Republic of Korea c Korea Information Society Development Institute (KISDI), Jincheon, Republic of Korea b
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 4 August 2015 Accepted 5 August 2015 Available online 6 August 2015 Keywords: Online video services Media displacement Non-media activities Media consumption
a b s t r a c t With an increase in the users viewing video content through the Internet, the interest in the recent trend of the displacement of old media (e.g., TV, radio, or newspaper) by online video services has also increased. To examine this trend, this study analyzed the time people spent on online video services compared to the time they spent on old media. The study also assessed whether online video services reduced the time users participated in nonmedia activities. The results of the study indicated that the time spent on online video services negatively influenced (i.e., reduced) the time spent on old video media and nonmedia activities. However, it did not have a significant effect on the time spent on old non-video media. In addition, the time spent on online video services was found to exert a greater influence on the time spent on non-media activities when compared to old video media. One of the reasons why the time spent on online video services reduces the time spent on both old video media and non-media activities is that the time spent viewing movies or TV programs through online video services negatively influences the time spent on these activities. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction As Internet technologies have advanced considerably and broadband communication infrastructures have matured, the number of services that provide video content through the Internet has increased (Cha and Chan-Olmsted, 2012; Cha, 2013). Even companies that do not have their own media networks can expand their business by providing video content to users through the Internet (FCC, 2012). Furthermore, a number of users watch online video content on Internetconnected devices such as smart phones, tablet PCs, and computers (Bondad-Brown et al., 2012). In the United States, the number of subscribers to the online video service Netflix was 22.7 million as of June 2012; this is larger than the number of subscribers to Comcast, which was the top cable television (TV) service provider (FCC, 2013). Similarly, Hulu Plus had more than 1.5 million subscribers by the end of 2011, compared with fewer than 300,000 at the end of 2010; this figure rose to 2 million as of June 30, 2012 (FCC, 2013). The time users spend on online video services is also increasing. For the singlemonth period of July 2012, more than 180 million Internet users watched online video content for an average of 20.6 h per viewer (Comscore, 2012; FCC, 2013).
⇑ Corresponding author. E-mail addresses: [email protected] (S.Y. Lee), [email protected], [email protected] (S.W. Lee). http://dx.doi.org/10.1016/j.tele.2015.08.002 0736-5853/Ó 2015 Elsevier Ltd. All rights reserved.
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Since both the number of online video services users and the time they spend viewing online videos have increased, there has been growing interest in the displacement effect of online compared to offline video services, the latter of which includes media such as terrestrial or pay TV (Cha, 2013; Cha and Chan-Olmsted, 2012; FCC, 2015). In the United States, some research firms have claimed that cord-cutting, which refers to subscribers canceling their cable TV subscriptions, has increased due to the increase in online video services (FCC, 2012). The TV penetration rate for all American households decreased to 96% during 2013–2014 from 99% during 2010–2011. According to the Federal Communications Commission (2015), this drop was precipitated by online video services. If there is only partial competition between a new medium and an old medium, competitive displacement may occur between the two media. Conversely, if a new medium dominates the competition, competitive exclusion may occur in the marketplace (Dimmick, 2003). Therefore, the displacement effect that online video services have on old media is considered a critical issue in the media industry (FCC, 2012, 2013). This study examined whether there is a displacement effect in the time spent watching video services online compared to the time spent using old media in South Korea. Old media can be classified into video media (examples include terrestrial TV, cable TV, IPTV, satellite TV, and mobile TV (DMB)) and non-video media. Non-video media includes radio, newspaper, games, and social media. This study also examined whether the displacement effect that online video services have on old media differs according to the types of content provided by online video services. The displacement effect hypothesis assumes that one day is limited to 24 h, thus the time users spend on media is also limited (Dutta-Bergman, 2004; Kayany and Yelsma, 2000; Mutz et al., 1993). Therefore, the more time users spend on a new medium, the less time they will devote to the previously used old media (Kayany and Yelsma, 2000; Mutz et al., 1993). However, Neuman et al. (2012) suggested that the total amount of time people spend on media was increasing. If users reduce the time spent on non-media activities to make time for new media, the total time spent on media use will increase, while the time spent on old media may be reduced slightly or not at all (Mutz et al., 1993). Therefore, this study also examined whether the use of online media services reduced the time spent on non-media activities. If the time spent on non-media activities decreases, this may be because people watch online video services during time they previously devoted to other pastimes, as well as during their existing dedicated media time (Mutz et al., 1993). This explains Neuman et al.’s (2012) claim that the introduction of new media increased the total time spent on different media. Conversely, if online video services do not affect the time people spend on non-media activities, it would suggest that users only spend the time they would usually dedicate to media activities on these online services. The present study is of significant value, since users are increasingly accessing online video services via Internetconnected devices such as smart phones, tablet PCs, and smart TVs (FCC, 2012). Users can access the video content that they want to watch through the Internet on these devices regardless of time and space (Bondad-Brown et al., 2012). In other words, online video services allow users to control their consumption of video content. This means that the diffusion of online video services may change users’ media usage pattern from push media (i.e., existing video media) to pull media (i.e., online video services) (Neuman et al., 2012). The study has implications not only for academia related to media displacement research but also for the industry, as it will shed light on the competitive relationships between different types of media services. If users reduce the time spent on old video media to increase the time spent on online video services, revenues of online video services will replace those of old video media in the long-term (Dimmick, 2003). This implies that online video services may have the potential to be substitutes for old video media (FCC, 2012). Analyzing the effect of online video services on non-media activities will also have academic implications, as it will empirically verify whether the time spent on online media increases the total time users spend on all media (Neuman et al., 2012). The remainder of the study proceeds as follows. Section 2 considers previous studies on the theories of media displacement and describes the research questions (RQs). Section 3 explains the research methods and Section 4 describes the study results. Section 5 explains the implications of the study results, and outlines the limitations.
2. Literature review and research questions 2.1. Media displacement theory When a new medium is functionally similar, yet superior, to an old medium, users will increase the time they spend on the former and decrease the time spent on the latter (Cha, 2013; Cha and Chan-Olmsted, 2012). Kayany and Yelsma (2000) argued that online media can replace TV in terms of informational function because it comparatively satisfies more informational needs. Lin (1994, 2001) suggested that when a new medium is considered more functionally desirable than an old medium, the audience may abandon the old medium altogether and replace it with the new. Therefore, when a new medium functionally displaces an old medium, it can reduce the time spent on the old one (Dimmick et al., 2000; Kang and Atkin, 1999; Kaye and Johnson, 2003; Lin, 1994); this describes the time displacement effect. Many studies on the time displacement effect have assumed that the amount of time available to use various types of media is limited because time budgets are finite entities (Cha, 2013; Dutta-Bergman, 2004; Kayany and Yelsma, 2000; Mutz et al., 1993).
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A number of studies have demonstrated the time displacement effect in relation to the emergence of a new medium. Coffin (1955) argued that watching TV reduced the time people spent on other media activities such as listening to the radio and reading books. Robinson (1981) cited that people who spent more time watching TV dedicated less time to other activities such as listening to the radio, shopping, attending parties, and traveling. James et al. (1995) suggested that the time spent using online bulletin boards reduced the time spent watching TV, reading books, and talking on the phone. Kayany and Yelsma (2000) asserted that online media reduced the time people spent watching TV, talking on the phone, and reading newspapers, while Lee and Kuo (2002) stated that the time Singaporean teenagers spent on the Internet displaced the time they spent watching TV. On the contrary, some studies have suggested that the use of a new medium does not affect, and may even effectively increase, the time spent on the old media. For instance, Robinson (1981) argued that the time people spent watching TV increased the time spent on at-home activities such as sleeping, listening to the radio, and reading newspapers, while it decreased the time they spent on away-from-home activities. Robinson and Kestnbaum (1999) suggested that people who used the Internet frequently engaged in other activities such as attending art events, reading books, watching movies, and playing sports. Neuman et al. (2012) highlighted that the total supply of media or information increased as an order of magnitude and that users found it difficult to choose among the range of different content options. This leads users to favor media whose content they can control (pull media), rather than media whose content is determined by service providers (push media). Even TVs that provides video content are evolving into intelligent digital devices with content recommendation systems for users (Adomavicius and Tuzhilin, 2005; Neuman et al., 2012). In addition, the number of people who are accessing video content through the Internet (pull media) has increased (Bondad-Brown et al., 2012). Given this transition from push to pull media dynamics, it is important to analyze how the use of online video services affects that of old media. Online video services are very similar to old video media such as terrestrial TV, pay TV, and DMB, as they provide video content for users. Therefore, it is reasonable to suggest that online video services are functionally displacing old video media (Cha, 2013; Cha and Chan-Olmsted, 2012). However, although the number of online video services users is increasing (Bondad-Brown et al., 2012) and the industry is focusing on the displacement effect that online video services have on cable TV (FCC, 2012), there has been limited academic research that has addressed whether or how the time spent on online video services has affected the time spent on old video media. As noted previously, this study classified old media into video media and non-video media and analyzed the impact that time spent on online video services has on the time spent on video media and non-video media. Video media are defined as media that provide video content for users. In South Korea, video media currently include terrestrial TV, cable TV, IPTV, satellite TV, and DMB. Non-video media include other forms, such as radio, newspaper, games, and social media. Based on this discussion, the first set of research questions for the study are as follows: RQ1. How does the time spent on online video services affect the time spent on old media? RQ1-1. How does the time spent on online video services affect the time spent on old video media? RQ1-2. How does the time spent on online video services affect the time spent on old non-video media? 2.2. The effect of the time spent on online video services on non-media activities Although users’ limited available time suggests that they will reduce the time spent on old media activities if a new medium is introduced into their lives (Kayany and Yelsma, 2000; Mutz et al., 1993), some studies have discovered that new media did not impact the time spent on old media, or even increased it. This could be due to the following reasons. First, as previously mentioned, if new media are not functionally similar or superior to old media, the increased time spent on new media will not affect the time spent on old media (Kayany and Yelsma, 2000; Kaye and Johnson, 2003). Second, when one type of media does not require much immersion and can be used simultaneously with other media, the increased time spent on that media will not reduce the time spent on other media (Mutz et al., 1993). Robinson (1969), and Kubey and Csikszentmihalyi (1990) argued that 30% of time spent watching TV was also spent simultaneously on other activities such as reading. Third, for users with high interests or involvement in specific fields or topics, the increased time spent on new media to access the content related to those areas may not decrease the time spent on old media (Dutta-Bergman, 2004). For example, users who are interested in baseball may watch baseball games or access baseball news through the Internet, but still consume baseball content through TV or newspapers. Therefore, the emergence of a new medium (the Internet) does not reduce the time spent on old media (TV and newspapers) (Dutta-Bergman, 2004). Fourth, if the time spent on new media reduces the time spent on activities that do not involve the use of media, the increased time spent on new media will not affect the time spent on old media (Mutz et al., 1993). Mutz et al. (1993) cited that researchers studying the time displacement effect must account for the time available for all activities; however, some activities are often ignored in the literature. Weiss (1969) concluded that the reduced time spent on most other media after the emergence of TV was smaller than the increased time spent on TV, due to the decrease in time spent on other activities, which were not explicitly addressed by Weiss’ research. Neuman et al. (2012) found out that the total time users spent on all media was gradually increasing. Through a longitudinal analysis that used the data collected over 46 years, the authors concluded that there has been modest and linear growth in total time spent on media. The decrease in the time spent on non-media activities equates to an increase in
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the total time spent on all media. Therefore, one of the reasons that overall media consumption is increasing might result from the decrease in non-media activity participation due to the emergence of new media. This study examined whether the time spent on online video services decreased that spent on non-media activities. The research question arising from this will explain why the total time spent on media consumption is gradually increasing, as Neuman et al. (2012) suggested. This study will also help to identify the media displacement phenomena related to online video services. Therefore, the second research question is follows: RQ2. How does the time spent on online video services affect the time spent on non-media activities? 2.3. Types of content and media displacement As previously mentioned, if new media functions are more satisfactory than those of old media, new media will displace old media (Cha, 2013; Cha and Chan-Olmsted, 2012; Lin, 1994). However, Cha (2013) argued that the functions satisfied by video services varied according to the video content genres. For example, among genres that were provided through online video platforms, comedies and dramas satisfied the desire for relaxation; news and entertainment programs satisfied the desire for boredom relief; and preview/recaps and documentaries satisfied the desire to access updates on current events (Cha, 2013). Therefore, it is expected that the media that might be displaced by online video services will vary according to the types of video content provided through online video services. However, most studies that have investigated the media displacement phenomena have assumed that the use of media is homogeneous. Therefore, extant studies have only focused on how much time people consumed on media and have not considered the fact that the media consumption can vary according to the types of content (Dutta-Bergman, 2004). For this reason, Dutta-Bergman (2004) suggested that studies on media displacement effect should analyze displacement phenomena in the context of content types. This study examined whether the time spent on online video services affected that spent on other media use and nonmedia activities in relation to types of content. The types of content provided by online video services considered in this study were TV programs, movies, and user-generated content (UGC). In light of the prior discussion, the remaining research questions are as follows: RQ3. How does the time spent on online video services affect the time spent on old media in the context of the types of content (TV programs, movies, and UGC) provided by online video services? RQ3-1. How does the time spent on online video services affect the time spent on old video media in the context of the types of content (TV programs, movies, and UGC) provided by online video services? RQ3-2. How does the time spent on online video services affect the time spent on old non-video media in the context of the types of content (TV programs, movies, and UGC) provided by online video services? RQ4. How does the time spent on online video services affect the time spent on non-media activities in the context of the types of content (TV programs, movies, and UGC) provided by online video services? 3. Method 3.1. Data collection To answer the research questions, this study utilized media diary data collected by the Korea Information Society Development Institute (KISDI, 2013). KISDI’s media diary survey asked participants to record their media use every 15 min for three days each year. In their media diaries, participants recorded the devices, the types of content, and connection method (i.e., the network through which they accessed the content). The media diary survey was conducted from 2010 to 2012 using household samples from across the country. To identify survey participants, a stratified two-stage sampling with probability proportionate to size was used. In the first stage, KISDI selected a proportionate number of enumeration district samples for every administrative district. In the second stage, household samples were selected from the identified enumeration districts proportionate to the number of households within the districts. The final survey sample consisted of 6737 participants in 2010, 12,000 participants in 2011 and 10,319 participants in 2012. A total of 3053 people participated in all three surveys from 2010 to 2012. The present study utilized the survey results from these 3053 participants to examine the displacement effect between online video services and other activities. 3.2. Measurement Online video simply refers to video content that is available over the Internet (Bondad-Brown et al., 2012). Therefore, this study defines online video services as services that offer video content to users through the Internet (FCC, 2012). The time spent on online video services was measured separately for the types of content provided through online video services, i.e., TV programs, movies, and UGC. The time spent on online video services was derived from the amount of time spent on these three types of content.
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S.Y. Lee et al. / Telematics and Informatics 33 (2016) 247–255 Table 1 Descriptive statistics (N = 3053). N
Percentage (%)
Age
Demographic variables 6–9 10–19 20–29 30–39 40–49 50–59 60–69 70 (or older)
150 417 268 611 562 386 331 328
4.9 13.7 8.8 20.0 18.4 12.6 10.8 10.7
Gender
Male Female
1295 1758
42.4 57.6
Education
No education Preschool Elementary school graduate Middle school graduate High school graduate College graduate Postgraduate
108 47 509 404 968 930 87
3.5 1.5 16.7 13.2 31.7 30.5 2.8
Monthly income
No income Below 500,000
1548 169
50.7 5.5
(KRW)
500,000–1 million 1–2 million 2–3 million 3–4 million 4–5 million Above 5 million
232 461 351 148 80 64
7.6 15.1 11.5 4.8 2.6 2.1
3053
100.0
Total
Old media included media that had been considered in prior displacement studies: TV (Coffin, 1955; Kayany and Yelsma, 2000; Robinson, 1981); radio (Coffin, 1955; Kaplan, 1978; Mutz et al., 1993); and newspapers (Dutta-Bergman, 2004; Kayany and Yelsma, 2000; Robinson, 1981). It also included media that had been the focus of prior studies which have considered the media users’ behaviors, such as DMB (Lee et al., 2011; Schuurman et al., 2009; Shin, 2006); games (Chang, 2013; Park et al., 2014); and social media (Cirucci, 2013; Klein et al., 2015; Parveen et al., 2015). TV was classified as terrestrial TV, cable TV, IPTV, and satellite TV. Old media were classified into video media and non-video media. The time spent on video media was measured by the time spent on TV (terrestrial TV, cable TV, IPTV, satellite TV) and DMB. The time spent on non-video media was measured by the time spent on radio, newspapers, games, and social media. The time spent on non-media activities was measured by subtracting the time spent on online video services and old media from three days (4320 min) each year. In terms of demographic characteristics, the participants’ ages were measured on an eight-point scale (1 = 6–9 years old; 2 = 10–19; 3 = 20–29; 4 = 30–39; 5 = 40–49; 6 = 50–59; 7 = 60–69; and 8 = 70 or older). Gender was measured with 0 for male and 1 for female. Education was measured on a seven-point scale from 0 to 6 (0 = not educated; 1 = preschooler; 2 = elementary school graduate; 3 = middle school graduate; 4 = high school graduate; 5 = college graduate; and 6 = postgraduate). Monthly income in Korean Republic Won (KRW) was measured on an eight-point scale (1 = no income; 2 = below 500,000 won [approximately $459 USD as of June 2015]; 3 = 500,000–1 million [approximately $917 USD]; 4 = 1 million–2 million [approximately $1834 USD]; 5 = 2 million–3 million [approximately $2751 USD]; 6 = 3 million–4 million [approximately $3668 USD]; 7 = 4 million–5 million [approximately $4585 USD]; and 8 = above 5 million).
3.3. Participants As shown in Table 1, the majority of the 3053 participants were in their 30s (20.0%) and 40s (18.4%). There were more female (57.6%) than male (42.4%) participants. In terms of education, most respondents reported having graduated from high school (31.7%), followed by having graduated college (30.5%). For monthly income, people with no income accounted for 50.7%, which represented more than half of the total number of participants. This was followed by participants who reported their incomes in the ranges of 1–2 million (15.1%) and 2–3 million (11.5%). The reason that there was a high percentage of participants without any income stems from the high percentage of participants in the under 20 (18.6%) and over 60 (21.5%) ages groups, both of which reflect low economic participation. Demographic factors may have some influences on the time spent on old video media such as TV, or the time spent on other activities. Age has been found to be positively associated with TV viewing (Kaye and Johnson, 2003; Bondad-Brown et al., 2012), but negatively associated with other activities, such as Internet usage (Bondad-Brown et al., 2012), and physical
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Table 2 The number of media users and the average time spent per day (N = 3053). Media
2010
2011
2012
Number of users
Average time spent per day Number of (SD) users
Average time spent per day Number of (SD) users
Average time spent per day (SD)
226
68.9 (113.2)
199
71.6 (90.7)
194
51.6 (60.5)
Video media Terrestrial TV Cable TV IPTV Satellite TV DMB
788 2092 213 127 62
178.1 (144.8) 208.0 (158.9) 159.8 (131.5) 177.8 (164.4) 50.5 (63.1)
693 2151 238 112 69
173.9 (136.3) 212.8 (146.5) 180.6 (136.7) 136.9 (124.2) 55.4 (69.0)
832 1984 312 108 50
151.5 (117.3) 205.0 (139.6) 167.3 (110.5) 138.4 (155.9) 33.9 (26.1)
Non-video media Radio Newspaper Game Social media
334 739 540 150
100.7 (133.9) 39.0 (40.8) 72.7 (76.4) 43.4 (57.5)
317 748 550 166
88.8 45.6 63.9 49.8
298 620 532 191
87.3 41.4 60.9 60.0
3053
1078.8 (216.7)
3053
1041.1 (40.5)
3053
1055.7 (193.3)
Online video services
Non-media activities
(125.6) (60.1) (55.6) (61.4)
(106.4) (46.0) (59.9) (84.6)
leisure activities (Mota and Esculcas, 2002; Pate et al., 1994). Income may negatively affect video media usage (Gorely et al., 2004; Sidney et al., 1996; Reid et al., 1980). Education may also negatively affect the time spent on video media (Sidney et al., 1996). In existing literature, gender has been generally not found to be significantly associated with video media usage, however, Mierlo and Bulck (2004) and Kahlor and Eastin (2011) found that women watched more video media than men. Men have been found to be more involved in other activities such as leisure activities, compared to women (Mota and Esculcas, 2002; Gallagher et al., 2012). Table 2 shows the number of participants using online video services was 226 in 2010, 199 in 2011, and 194 in 2012. The average number of minutes spent on online video services per day was 68.9 (standard deviation [SD] = 113.2); 71.6 (SD = 90.7); and 51.6 (SD = 60.5), respectively. During the three-year study period, the number of participants who watched online video services was higher than the number of participants who used satellite TV, DMB, and social media; conversely, this number was smaller than the number of participants who used terrestrial TV, cable TV, IPTV, radio, and games. The average time each user spent on online video services per day was less than half of the average time spent on terrestrial TV, cable TV, or IPTV per day. Cable TV had the highest number of users among all media: 2092 in 2010; 2151 in 2011; and 1984 in 2012. The average number of minutes these users spent on cable TV per day was 208.0 (SD = 158.9) in 2010; 212.8 (SD = 146.5) in 2011; and 205.0 (SD = 139.6) in 2012; this equated to the highest number of minutes among all of the media considered. The average number of minutes spent on non-media activities per day was 1078.8 (SD = 216.7) in 2010; 1041.1 (SD = 40.5) in 2011; and 1055.7 (SD = 193.3) in 2012, which equates to less than 18 h per day. 3.4. Statistical analysis The main objective of this study is to examine the effect of online video services viewing on both old media use and nonmedia activities. As we explained previously, we used panel data, which asked survey participants to record the media they used from 2010 to 2012. In this study, we calculated average values for three observations for each variable, and used these average values to obtain consistent media usage patterns for each observation. In order to test the effect of online video services viewing on both old media use and non-media activities, we employed the seemingly unrelated regression (SUR) model.1
4. Results Table 3 summarizes the statistical results. The coefficients presented in this table represent regression coefficients. As previous research has suggested, age positively affected video media usage, but negatively affected other activities, such as non-video media activities and non-media activities. Income negatively affected the time spent on video media. In terms of gender, it was found that women used video media more, but less involved other activities, than men. 1 The errors in a set of related regression equations are often correlated, and the efficiency of the estimates can be improved by taking these correlations into account (Cameron and Trivedi, 2005; Greene, 2000). The efficient estimator for the SUR model is generalized least squares (Zellner, 1962; Greene, 2000). Zellner’s study (1962) showed that regression coefficients, which were estimated simultaneously by applying Aitken’s generalized least-squares to the whole system, were more efficient than classical single-equation least-squares estimators. The SUR model fully relaxes the constraints on the coefficients, and assumes that each equation has its own fixed set of parameters.
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Model 1 revealed the effect of online video services viewing on both video media use, such as terrestrial TV, cable TV, IPTV, satellite TV, and DMB, and other activities that include non-video media use and non-media activities. The result indicated that the time spent on online video services negatively affected both the time spent on video media (b = .25, p < .01) and the time spent on other activities (b = .94, p < .01). This suggests that as people spend more time on watching online video services, they reduce both the usage of video media and participation in other activities. In Model 2, we subdivided old media into two parts, video media and non-video media, and examined the effect of the time spent on online video services on three dependent variables (video media, non-video media, and non-media activities). The results indicated that the time spent on online video services negatively affected both the time spent on video media (b = .197, p < .05) and non-media activities (b = .94, p < .01), but it did not affect the time spent on non-video media (b = .004, p > .05) (RQ1 and RQ2). Roughly, a one-minute increase in online video viewing reduces approximately .2 min in video media consumption and .9 min from other activities. Model 1 and Model 2 indicated that online video services viewing not only replaced old video media, but also became a part of the viewer’s activities by reducing non-media activities. Considering the sizes of the coefficients in Model 1 and Model 2, this finding implies an increase in the total time spent on media consumption. Interestingly, the effect of online video services on non-media activities was found to be greater than that of online video services on old video media.2 Model 3 subdivided online video services viewing into three categories (TV program viewing, movie viewing, and UGC viewing) and examined which types of content affected video media usage, non-video media usage, and non-media activities. The results indicated that watching movies provided by online video services negatively affected video media usage (b = .283, p < .05) (RQ3). Conversely, watching TV programs (b = 1.32, p < .01) and movies (b = .778, p < .01) provided by online video services negatively affected non-media activities (RQ4).
5. Conclusion This study examined whether online video services are displacing old media (video and non-video media) in terms of time. It also verified whether the use of online video services decreased the time spent on non-media activities; i.e., whether it increased the total time spent on all media (total media consumption) (Neuman et al., 2012). The results related to RQ1 indicated that the time spent on online video services negatively affected the time spent on old media in the sample observed. Specifically, the time spent on online video services negatively affected the time spent on old video media, but it did not affect the time spent on old non-video activities. The results are in line with those found by Kayany and Yelsma (2000) and Lee and Kuo (2002), who argued that Internet media has served to decrease the amount of time households spent on watching TV. From an industry perspective, these results suggest that online video services are now starting to form a competitive relationship with old video media such as TV, and could displace such media in the future, although online video services have not decreased the number of cable TV subscribers in South Korea as of yet. The results that users reduced the time spent on old video media such as TV to use online video services indicate their transition from push media to pull media (Neuman et al., 2012). Users used online video services—which can be accessed anywhere and anytime, and provide video content that they want to watch—instead of old video media, which can only be accessed at home and provide only the content chosen by service providers. In other words, consumption of video content is gradually changing in a manner that provides greater control to consumers. The results related to RQ3 revealed that among the various content available, watching movies provided by online video services decreased the time spent on old video media. In other words, the reason why online video services have a time displacement effect on old video media was that the more time users spent watching movies provided by online video services, the less they spent on old video media. Conversely, watching TV programs or UGC provided by online video services did not affect the time spent on old video media. These results indicated that displacement and complementary relationships can differ according to the types of content provided by a new medium, as argued by Dutta-Bergman (2004). Regarding RQ2 and RQ4, the time spent on online video services negatively affected the time spent on non-media activities in the sample. This suggests that in order to make time for online video services, people decrease not only the time they spend on old video media but also the time they spend on non-media activities (Himmelweit et al., 1958; Mutz et al., 1993; Weiss, 1969). Therefore, this result revealed that the time spent on online video services increased the total media consumption. Meanwhile, online video services decreased the time spent on non-media activities more than the time spent on old video media. This suggests that people decrease the time they spend on non-media activities rather than old video media to make time to view online video services. The reason why online video services decreased the time spent on non-media activities was that watching TV programs and movies provided by online video services decreased the time spent on non-media activities. In other words, watching TV programs and movies through online video services increases the total media consumption. These results explain one of the reasons behind Neuman et al.’s (2012) findings, which suggested that the total media consumption of American households was gradually increasing. With the emergence of new media such as online video services, users reduce the time they spent on non-media activities to make time to use the new media. 2 According to Chi-square test, the coefficient of online video services viewing on old video media use was statistically different from that of online video services viewing on non-media activities.
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Table 3 SUR results (N = 3053). Model 1 Video media Online video Online video (TV) Online video (movie) Online video (UGC) Age Income Education Gender R2 Obs. * **
.25 (.094)**
Model 2 Other activities Video media .94 (.116)**
.197 (.099)*
Model 3 Non-video media Non-media .004 (.046)
Video media .004 (.186)
.004 (.086)
1.32 (.235)**
.283 (.12)*
.009 (.056)
.778 (.151)**
.018 (1.1) .939 (.116)** 6.53 (.186)** 2.03 (.214)** .026 (.23) .764 (.293)** 3.87 (.364)** 6.25 (.774)** 7.67 (.959)** .36 .19 3053 3053
5.19 (.99)* 1.9 (.227)** .378 (.31) 4.08 (.818)** .23 3053
.86 (.091)** .066 (.105) .653 (.144)** 3.29 (.381)** .07 3053
Non-video media Non-media
.94 (.125)**
.934 (.125)** .093 (.286) 4.52 (.391)** 4.37 (1.03)** .13 3053
.374 (.374)
5.18 (.196)** .858 (.091)** 1.91 (.227)** .067 (.105) 3.89 (.31) .65 (.144)** ** 4.05 (.818) 3.29 (.381)** .24 .07 3053 3053
.344 (.1399) 4.22 (.247)** .118 (.286) 4.51 (.391)** 4.31 (1.03)** .13 3053
Significant at 5%. Significant at 1%.
Conversely, due to time (Kayany and Yelsma, 2000; Mutz et al., 1993) and attention (Neuman et al., 2012) constraints, it is expected that the total time users spend on all media will not continue to increase. Neuman and associates also determined that the discrepancy between the total media supply and the total media consumption increased as the years passed because the total media supply was increasing exponentially, while the total media consumption was increasing linearly and modestly (Neuman et al., 2012). As there might be no technical limit on the supply of media (Neuman et al., 2012), it could increase exponentially. However, as there are only 24 h in a day, media consumption cannot increase dramatically. With the appearance of a new medium, people consume part of the time that would have been spent on non-media activities. However, they do not have unlimited time to use a new medium. There are a few limitations to this study. First, the media diary survey was conducted on only Korean participants, so it is unclear as to whether our findings could be generalized to other countries. By controlling the impact of a national background, further studies may collect data from different countries to generalize our results. Second, this study utilized secondary data that were collected by KISDI, thus there was a limitation in conducting an in-depth analysis. For example, this study classified the types of content as TV programs, movies, and UGC. However, for an in-depth analysis on the media displacement effect that online video services have on old media, it would be helpful to classify content into genres such as drama, news, documentary, comedy, education, and others (Cha, 2013). Third, the results from the media diary survey suggested that the actual amount of time spent on a certain media could have been overestimated if the users spent less time than 15 min on that media, as the respondents were asked to record the media use every 15 min. Finally, this study failed to consider various aspects of online video services such as the content linearity, devices to use the services, and pricing. Future research should consider other various aspects of online video services to examine the time displacement effect of that media. References Adomavicius, G., Tuzhilin, A., 2005. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17 (6), 734–749. Bondad-Brown, B.A., Rice, R.E., Pearce, K.E., 2012. Influences on TV viewing and online user-shared video use: demographics, generations, contextual age, media use, motivations, and audience activity. J. Broadcast. Electron. Media 56 (4), 471–493. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge. Cha, J., 2013. Does genre type influence choice of video platform? A study of college student use of internet and television for specific video genres. Telematics Inform. 30 (3), 189–200. Cha, J., Chan-Olmsted, S.M., 2012. Substitutability between online video platforms and television. Journal. Mass Commun. Q. 89 (2), 261–278. Chang, C.C., 2013. Examining users’ intention to continue using social network games: a flow experience perspective. Telematics Inform. 30 (4), 311–321. Cirucci, A.M., 2013. First person paparazzi: why social media should be studied more like video games. Telematics Inform. 30 (1), 47–59. Coffin, T., 1955. Television’s impact on society. Am. Psychol. 10 (10), 630–641. ComScore, 2012. ComScore releases July 2012 U.S. online video rankings. 2012. 8. 9. Dimmick, J.W., 2003. Media Competition and Coexistence: The Theory of the Niche. Lawrence Erlbaum Associates, Mahwah, NJ. Dimmick, J.W., Kline, S., Stafford, L., 2000. The gratification niches of personal e-mail and the telephone: competition, displacement, and complementarity. Commun. Res. 27 (2), 227–248. Dutta-Bergman, M.J., 2004. Complementarity in consumption of news types across traditional and new media. J. Broadcast. Electron. Media 48 (1), 41–60. FCC, 2012. Annual assessment of the status of competition in the market for the delivery of video programming. MB Docket No. 07-269, FCC 12-81. FCC, 2013. Annual assessment of the status of competition in the market for the delivery of video programming. MB Docket No. 12-203, FCC 13-99. FCC, 2015. Annual assessment of the status of competition in the market for the delivery of video programming. MB Docket No. 14-16, FCC 15-41. Gallagher, P., Yancy Jr., W.S., Swartout, K., Denissen, J.J.A., Kuhnel, A., Voils, C.I., 2012. Age and sex differences in prospective effects of health goals and motivations on daily leisure-time physical activity. Prev. Med. 55, 322–324. Gorely, T., Marshall, S.J., Biddle, S.J.H., 2004. Couch kids: correlates of television viewing among youth. Int. J. Behav. Med. 11 (3), 152–163. Greene, W., 2000. Econometric Analysis. Prentice Hall, NJ. Himmelweit, H.T., Oppenheim, A.N., Vince, P., 1958. Television and the Child. Oxford University Press, London.
S.Y. Lee et al. / Telematics and Informatics 33 (2016) 247–255
255
James, M.L., Wotring, C.E., Forrest, E.J., 1995. An exploratory study of the perceived benefits of electronic bulletin board use and their impact on other communication activities. J. Broadcast. Electron. Media 39, 30–50. Kahlor, L., Eastin, M.S., 2011. Television’s role in the culture of violence toward women: a study of television viewing and the cultivation of rape myth acceptance in the United States. J. Broadcast. Electron. Media 55 (2), 215–231. Kang, M., Atkin, D.J., 1999. Exploring the role of media uses and gratifications in multimedia cable adoption. Telematics Inform. 16, 59–74. Kaplan, S.J., 1978. The impact of cable television services on the use of competing media. J. Broadcast. 22, 155–165. Kayany, J.M., Yelsma, P., 2000. Displacement effects of online media in the socio-technical contexts of households. J. Broadcast. Electron. Media 44 (2), 215– 229. Kaye, B.K., Johnson, T.J., 2003. From here to obscurity?: Media substitution theory and traditional media in an on-line world. J. Am. Soc. Inform. Sci. Technol. 54 (3), 260–273. KISDI STAT Homepage. Retrieved November 16, 2013. Klein, A., Ahlf, H., Sharma, V., 2015. Social activity and structural centrality in online social networks. Telematics Inform. 32 (2), 321–332. Kubey, R., Csikszentmihalyi, M., 1990. Television and the Quality of Life: How Viewing Shapes Everyday Experience. Lawrence Erlbaum, Hillsdale, NJ. Lee, W., Kuo, E.C.Y., 2002. Internet and displacement effect: children’s media use and activities in Singapore. J. Comput. Mediat. Commun. 7 (2). Lee, H., Kim, D., Ryu, J., Lee, S., 2011. Acceptance and rejection of mobile TV among young adults: a case of college students in South Korea. Telematics Inform. 28 (4), 239–250. Lin, C.A., 1994. Audience fragmentation in a competitive video marketplace. J. Advert. Res. 34 (6), 1–17. Lin, C.A., 2001. Audience attributes, media supplementation, and likely online service adoption. Mass Commun. Soc. 4 (1), 19–38. Mierlo, J.V., Bulck, J.V., 2004. Benchmarking the cultivation approach to video game effects: a comparison of the correlates of TV viewing and game play. J. Adolesc. 27, 97–111. Mota, J., Esculcas, C., 2002. Leisure-time physical activity behavior: structured and unstructured choices according to sex, age, and level of physical activity. Int. J. Behav. Med. 9 (2), 111–121. Mutz, D.C., Roberts, D.F., Vuuren, D.P., 1993. Reconsidering the displacement hypothesis: television’s influence on children’s time use. Commun. Res. 20 (1), 51–75. Neuman, W.R., Park, Y.J., Panek, E., 2012. Tracking the flow of information into the home: an empirical assessment of the digital revolution in the United States. Int. J. Commun. 6, 1022–1041. Park, E., Baek, S., Ohm, J., Chan, H.J., 2014. Determinants of player acceptance of mobile social network games: an application of extended technology acceptance model. Telematics Inform. 31 (1), 3–15. Parveen, F., Jaafar, N.I., Ainin, S., 2015. Social media usage and organizational performance: reflections of Malaysian social media managers. Telematics Inform. 32 (1), 67–78. Pate, R.R., Long, B.J., Heath, G., 1994. Descriptive epidemiology of physical activity in adolescents. Pediatr. Exerc. Sci. 6, 434–447. Reid, L.N., Bearden, W.O., Teel, J.E., 1980. Family income, TV viewing and children’s cereal ratings. Journal. Q. 57 (2), 327–330. Robinson, J.P., 1969. Television and leisure time: yesterday, today, and (maybe) tomorrow. Public Opin. Q. 33 (2), 210–222. Robinson, J.P., 1981. Television and leisure time: a new scenario. J. Commun. 31 (1), 120–130. Robinson, J.P., Kestnbaum, M., 1999. The personal computer, culture and other uses of free time. Soc. Sci. Comput. Rev. 17 (2), 209–216. Schuurman, D., Marez, L.D., Veevaete, P., Evens, T., 2009. Content and context for mobile television: integrating trial, expert and user findings. Telematics Inform. 26 (3), 293–305. Shin, D.H., 2006. Prospectus of mobile TV: another bubble or killer application? Telematics Inform. 23 (4), 253–270. Sidney, S., Sternfeld, B., Haskell, W.L., Jacobs, D.R., Chesney, M.A., Hulley, S.B., 1996. Television viewing and cardiovascular risk factors in young adults: the CARDIA study. Ann. Epidemiol. 6 (2), 154–159. Weiss, W., 1969. Effects of the mass media of communications. Handbook of Social Psychology: Applied Social Psychology, vol. 5. Addison-Wesley, MA. Zellner, A., 1962. An efficient method of estimating seemingly unrelated regressions and tests of aggregation bias. J. Am. Stat. Assoc. 57, 500–509.
Contents lists available at ScienceDirect
Telematics and Informatics journal homepage: www.elsevier.com/locate/tele
Time displacement effect of online video services on other media in South Korea Seung Yeop Lee a,b, Sang Woo Lee a,⇑, Changwan Kim c a
Graduate School of Information, Yonsei University, Seoul, Republic of Korea Korea Communications Agency (KCA), Naju, Republic of Korea c Korea Information Society Development Institute (KISDI), Jincheon, Republic of Korea b
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 4 August 2015 Accepted 5 August 2015 Available online 6 August 2015 Keywords: Online video services Media displacement Non-media activities Media consumption
a b s t r a c t With an increase in the users viewing video content through the Internet, the interest in the recent trend of the displacement of old media (e.g., TV, radio, or newspaper) by online video services has also increased. To examine this trend, this study analyzed the time people spent on online video services compared to the time they spent on old media. The study also assessed whether online video services reduced the time users participated in nonmedia activities. The results of the study indicated that the time spent on online video services negatively influenced (i.e., reduced) the time spent on old video media and nonmedia activities. However, it did not have a significant effect on the time spent on old non-video media. In addition, the time spent on online video services was found to exert a greater influence on the time spent on non-media activities when compared to old video media. One of the reasons why the time spent on online video services reduces the time spent on both old video media and non-media activities is that the time spent viewing movies or TV programs through online video services negatively influences the time spent on these activities. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction As Internet technologies have advanced considerably and broadband communication infrastructures have matured, the number of services that provide video content through the Internet has increased (Cha and Chan-Olmsted, 2012; Cha, 2013). Even companies that do not have their own media networks can expand their business by providing video content to users through the Internet (FCC, 2012). Furthermore, a number of users watch online video content on Internetconnected devices such as smart phones, tablet PCs, and computers (Bondad-Brown et al., 2012). In the United States, the number of subscribers to the online video service Netflix was 22.7 million as of June 2012; this is larger than the number of subscribers to Comcast, which was the top cable television (TV) service provider (FCC, 2013). Similarly, Hulu Plus had more than 1.5 million subscribers by the end of 2011, compared with fewer than 300,000 at the end of 2010; this figure rose to 2 million as of June 30, 2012 (FCC, 2013). The time users spend on online video services is also increasing. For the singlemonth period of July 2012, more than 180 million Internet users watched online video content for an average of 20.6 h per viewer (Comscore, 2012; FCC, 2013).
⇑ Corresponding author. E-mail addresses: [email protected] (S.Y. Lee), [email protected], [email protected] (S.W. Lee). http://dx.doi.org/10.1016/j.tele.2015.08.002 0736-5853/Ó 2015 Elsevier Ltd. All rights reserved.
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Since both the number of online video services users and the time they spend viewing online videos have increased, there has been growing interest in the displacement effect of online compared to offline video services, the latter of which includes media such as terrestrial or pay TV (Cha, 2013; Cha and Chan-Olmsted, 2012; FCC, 2015). In the United States, some research firms have claimed that cord-cutting, which refers to subscribers canceling their cable TV subscriptions, has increased due to the increase in online video services (FCC, 2012). The TV penetration rate for all American households decreased to 96% during 2013–2014 from 99% during 2010–2011. According to the Federal Communications Commission (2015), this drop was precipitated by online video services. If there is only partial competition between a new medium and an old medium, competitive displacement may occur between the two media. Conversely, if a new medium dominates the competition, competitive exclusion may occur in the marketplace (Dimmick, 2003). Therefore, the displacement effect that online video services have on old media is considered a critical issue in the media industry (FCC, 2012, 2013). This study examined whether there is a displacement effect in the time spent watching video services online compared to the time spent using old media in South Korea. Old media can be classified into video media (examples include terrestrial TV, cable TV, IPTV, satellite TV, and mobile TV (DMB)) and non-video media. Non-video media includes radio, newspaper, games, and social media. This study also examined whether the displacement effect that online video services have on old media differs according to the types of content provided by online video services. The displacement effect hypothesis assumes that one day is limited to 24 h, thus the time users spend on media is also limited (Dutta-Bergman, 2004; Kayany and Yelsma, 2000; Mutz et al., 1993). Therefore, the more time users spend on a new medium, the less time they will devote to the previously used old media (Kayany and Yelsma, 2000; Mutz et al., 1993). However, Neuman et al. (2012) suggested that the total amount of time people spend on media was increasing. If users reduce the time spent on non-media activities to make time for new media, the total time spent on media use will increase, while the time spent on old media may be reduced slightly or not at all (Mutz et al., 1993). Therefore, this study also examined whether the use of online media services reduced the time spent on non-media activities. If the time spent on non-media activities decreases, this may be because people watch online video services during time they previously devoted to other pastimes, as well as during their existing dedicated media time (Mutz et al., 1993). This explains Neuman et al.’s (2012) claim that the introduction of new media increased the total time spent on different media. Conversely, if online video services do not affect the time people spend on non-media activities, it would suggest that users only spend the time they would usually dedicate to media activities on these online services. The present study is of significant value, since users are increasingly accessing online video services via Internetconnected devices such as smart phones, tablet PCs, and smart TVs (FCC, 2012). Users can access the video content that they want to watch through the Internet on these devices regardless of time and space (Bondad-Brown et al., 2012). In other words, online video services allow users to control their consumption of video content. This means that the diffusion of online video services may change users’ media usage pattern from push media (i.e., existing video media) to pull media (i.e., online video services) (Neuman et al., 2012). The study has implications not only for academia related to media displacement research but also for the industry, as it will shed light on the competitive relationships between different types of media services. If users reduce the time spent on old video media to increase the time spent on online video services, revenues of online video services will replace those of old video media in the long-term (Dimmick, 2003). This implies that online video services may have the potential to be substitutes for old video media (FCC, 2012). Analyzing the effect of online video services on non-media activities will also have academic implications, as it will empirically verify whether the time spent on online media increases the total time users spend on all media (Neuman et al., 2012). The remainder of the study proceeds as follows. Section 2 considers previous studies on the theories of media displacement and describes the research questions (RQs). Section 3 explains the research methods and Section 4 describes the study results. Section 5 explains the implications of the study results, and outlines the limitations.
2. Literature review and research questions 2.1. Media displacement theory When a new medium is functionally similar, yet superior, to an old medium, users will increase the time they spend on the former and decrease the time spent on the latter (Cha, 2013; Cha and Chan-Olmsted, 2012). Kayany and Yelsma (2000) argued that online media can replace TV in terms of informational function because it comparatively satisfies more informational needs. Lin (1994, 2001) suggested that when a new medium is considered more functionally desirable than an old medium, the audience may abandon the old medium altogether and replace it with the new. Therefore, when a new medium functionally displaces an old medium, it can reduce the time spent on the old one (Dimmick et al., 2000; Kang and Atkin, 1999; Kaye and Johnson, 2003; Lin, 1994); this describes the time displacement effect. Many studies on the time displacement effect have assumed that the amount of time available to use various types of media is limited because time budgets are finite entities (Cha, 2013; Dutta-Bergman, 2004; Kayany and Yelsma, 2000; Mutz et al., 1993).
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A number of studies have demonstrated the time displacement effect in relation to the emergence of a new medium. Coffin (1955) argued that watching TV reduced the time people spent on other media activities such as listening to the radio and reading books. Robinson (1981) cited that people who spent more time watching TV dedicated less time to other activities such as listening to the radio, shopping, attending parties, and traveling. James et al. (1995) suggested that the time spent using online bulletin boards reduced the time spent watching TV, reading books, and talking on the phone. Kayany and Yelsma (2000) asserted that online media reduced the time people spent watching TV, talking on the phone, and reading newspapers, while Lee and Kuo (2002) stated that the time Singaporean teenagers spent on the Internet displaced the time they spent watching TV. On the contrary, some studies have suggested that the use of a new medium does not affect, and may even effectively increase, the time spent on the old media. For instance, Robinson (1981) argued that the time people spent watching TV increased the time spent on at-home activities such as sleeping, listening to the radio, and reading newspapers, while it decreased the time they spent on away-from-home activities. Robinson and Kestnbaum (1999) suggested that people who used the Internet frequently engaged in other activities such as attending art events, reading books, watching movies, and playing sports. Neuman et al. (2012) highlighted that the total supply of media or information increased as an order of magnitude and that users found it difficult to choose among the range of different content options. This leads users to favor media whose content they can control (pull media), rather than media whose content is determined by service providers (push media). Even TVs that provides video content are evolving into intelligent digital devices with content recommendation systems for users (Adomavicius and Tuzhilin, 2005; Neuman et al., 2012). In addition, the number of people who are accessing video content through the Internet (pull media) has increased (Bondad-Brown et al., 2012). Given this transition from push to pull media dynamics, it is important to analyze how the use of online video services affects that of old media. Online video services are very similar to old video media such as terrestrial TV, pay TV, and DMB, as they provide video content for users. Therefore, it is reasonable to suggest that online video services are functionally displacing old video media (Cha, 2013; Cha and Chan-Olmsted, 2012). However, although the number of online video services users is increasing (Bondad-Brown et al., 2012) and the industry is focusing on the displacement effect that online video services have on cable TV (FCC, 2012), there has been limited academic research that has addressed whether or how the time spent on online video services has affected the time spent on old video media. As noted previously, this study classified old media into video media and non-video media and analyzed the impact that time spent on online video services has on the time spent on video media and non-video media. Video media are defined as media that provide video content for users. In South Korea, video media currently include terrestrial TV, cable TV, IPTV, satellite TV, and DMB. Non-video media include other forms, such as radio, newspaper, games, and social media. Based on this discussion, the first set of research questions for the study are as follows: RQ1. How does the time spent on online video services affect the time spent on old media? RQ1-1. How does the time spent on online video services affect the time spent on old video media? RQ1-2. How does the time spent on online video services affect the time spent on old non-video media? 2.2. The effect of the time spent on online video services on non-media activities Although users’ limited available time suggests that they will reduce the time spent on old media activities if a new medium is introduced into their lives (Kayany and Yelsma, 2000; Mutz et al., 1993), some studies have discovered that new media did not impact the time spent on old media, or even increased it. This could be due to the following reasons. First, as previously mentioned, if new media are not functionally similar or superior to old media, the increased time spent on new media will not affect the time spent on old media (Kayany and Yelsma, 2000; Kaye and Johnson, 2003). Second, when one type of media does not require much immersion and can be used simultaneously with other media, the increased time spent on that media will not reduce the time spent on other media (Mutz et al., 1993). Robinson (1969), and Kubey and Csikszentmihalyi (1990) argued that 30% of time spent watching TV was also spent simultaneously on other activities such as reading. Third, for users with high interests or involvement in specific fields or topics, the increased time spent on new media to access the content related to those areas may not decrease the time spent on old media (Dutta-Bergman, 2004). For example, users who are interested in baseball may watch baseball games or access baseball news through the Internet, but still consume baseball content through TV or newspapers. Therefore, the emergence of a new medium (the Internet) does not reduce the time spent on old media (TV and newspapers) (Dutta-Bergman, 2004). Fourth, if the time spent on new media reduces the time spent on activities that do not involve the use of media, the increased time spent on new media will not affect the time spent on old media (Mutz et al., 1993). Mutz et al. (1993) cited that researchers studying the time displacement effect must account for the time available for all activities; however, some activities are often ignored in the literature. Weiss (1969) concluded that the reduced time spent on most other media after the emergence of TV was smaller than the increased time spent on TV, due to the decrease in time spent on other activities, which were not explicitly addressed by Weiss’ research. Neuman et al. (2012) found out that the total time users spent on all media was gradually increasing. Through a longitudinal analysis that used the data collected over 46 years, the authors concluded that there has been modest and linear growth in total time spent on media. The decrease in the time spent on non-media activities equates to an increase in
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the total time spent on all media. Therefore, one of the reasons that overall media consumption is increasing might result from the decrease in non-media activity participation due to the emergence of new media. This study examined whether the time spent on online video services decreased that spent on non-media activities. The research question arising from this will explain why the total time spent on media consumption is gradually increasing, as Neuman et al. (2012) suggested. This study will also help to identify the media displacement phenomena related to online video services. Therefore, the second research question is follows: RQ2. How does the time spent on online video services affect the time spent on non-media activities? 2.3. Types of content and media displacement As previously mentioned, if new media functions are more satisfactory than those of old media, new media will displace old media (Cha, 2013; Cha and Chan-Olmsted, 2012; Lin, 1994). However, Cha (2013) argued that the functions satisfied by video services varied according to the video content genres. For example, among genres that were provided through online video platforms, comedies and dramas satisfied the desire for relaxation; news and entertainment programs satisfied the desire for boredom relief; and preview/recaps and documentaries satisfied the desire to access updates on current events (Cha, 2013). Therefore, it is expected that the media that might be displaced by online video services will vary according to the types of video content provided through online video services. However, most studies that have investigated the media displacement phenomena have assumed that the use of media is homogeneous. Therefore, extant studies have only focused on how much time people consumed on media and have not considered the fact that the media consumption can vary according to the types of content (Dutta-Bergman, 2004). For this reason, Dutta-Bergman (2004) suggested that studies on media displacement effect should analyze displacement phenomena in the context of content types. This study examined whether the time spent on online video services affected that spent on other media use and nonmedia activities in relation to types of content. The types of content provided by online video services considered in this study were TV programs, movies, and user-generated content (UGC). In light of the prior discussion, the remaining research questions are as follows: RQ3. How does the time spent on online video services affect the time spent on old media in the context of the types of content (TV programs, movies, and UGC) provided by online video services? RQ3-1. How does the time spent on online video services affect the time spent on old video media in the context of the types of content (TV programs, movies, and UGC) provided by online video services? RQ3-2. How does the time spent on online video services affect the time spent on old non-video media in the context of the types of content (TV programs, movies, and UGC) provided by online video services? RQ4. How does the time spent on online video services affect the time spent on non-media activities in the context of the types of content (TV programs, movies, and UGC) provided by online video services? 3. Method 3.1. Data collection To answer the research questions, this study utilized media diary data collected by the Korea Information Society Development Institute (KISDI, 2013). KISDI’s media diary survey asked participants to record their media use every 15 min for three days each year. In their media diaries, participants recorded the devices, the types of content, and connection method (i.e., the network through which they accessed the content). The media diary survey was conducted from 2010 to 2012 using household samples from across the country. To identify survey participants, a stratified two-stage sampling with probability proportionate to size was used. In the first stage, KISDI selected a proportionate number of enumeration district samples for every administrative district. In the second stage, household samples were selected from the identified enumeration districts proportionate to the number of households within the districts. The final survey sample consisted of 6737 participants in 2010, 12,000 participants in 2011 and 10,319 participants in 2012. A total of 3053 people participated in all three surveys from 2010 to 2012. The present study utilized the survey results from these 3053 participants to examine the displacement effect between online video services and other activities. 3.2. Measurement Online video simply refers to video content that is available over the Internet (Bondad-Brown et al., 2012). Therefore, this study defines online video services as services that offer video content to users through the Internet (FCC, 2012). The time spent on online video services was measured separately for the types of content provided through online video services, i.e., TV programs, movies, and UGC. The time spent on online video services was derived from the amount of time spent on these three types of content.
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S.Y. Lee et al. / Telematics and Informatics 33 (2016) 247–255 Table 1 Descriptive statistics (N = 3053). N
Percentage (%)
Age
Demographic variables 6–9 10–19 20–29 30–39 40–49 50–59 60–69 70 (or older)
150 417 268 611 562 386 331 328
4.9 13.7 8.8 20.0 18.4 12.6 10.8 10.7
Gender
Male Female
1295 1758
42.4 57.6
Education
No education Preschool Elementary school graduate Middle school graduate High school graduate College graduate Postgraduate
108 47 509 404 968 930 87
3.5 1.5 16.7 13.2 31.7 30.5 2.8
Monthly income
No income Below 500,000
1548 169
50.7 5.5
(KRW)
500,000–1 million 1–2 million 2–3 million 3–4 million 4–5 million Above 5 million
232 461 351 148 80 64
7.6 15.1 11.5 4.8 2.6 2.1
3053
100.0
Total
Old media included media that had been considered in prior displacement studies: TV (Coffin, 1955; Kayany and Yelsma, 2000; Robinson, 1981); radio (Coffin, 1955; Kaplan, 1978; Mutz et al., 1993); and newspapers (Dutta-Bergman, 2004; Kayany and Yelsma, 2000; Robinson, 1981). It also included media that had been the focus of prior studies which have considered the media users’ behaviors, such as DMB (Lee et al., 2011; Schuurman et al., 2009; Shin, 2006); games (Chang, 2013; Park et al., 2014); and social media (Cirucci, 2013; Klein et al., 2015; Parveen et al., 2015). TV was classified as terrestrial TV, cable TV, IPTV, and satellite TV. Old media were classified into video media and non-video media. The time spent on video media was measured by the time spent on TV (terrestrial TV, cable TV, IPTV, satellite TV) and DMB. The time spent on non-video media was measured by the time spent on radio, newspapers, games, and social media. The time spent on non-media activities was measured by subtracting the time spent on online video services and old media from three days (4320 min) each year. In terms of demographic characteristics, the participants’ ages were measured on an eight-point scale (1 = 6–9 years old; 2 = 10–19; 3 = 20–29; 4 = 30–39; 5 = 40–49; 6 = 50–59; 7 = 60–69; and 8 = 70 or older). Gender was measured with 0 for male and 1 for female. Education was measured on a seven-point scale from 0 to 6 (0 = not educated; 1 = preschooler; 2 = elementary school graduate; 3 = middle school graduate; 4 = high school graduate; 5 = college graduate; and 6 = postgraduate). Monthly income in Korean Republic Won (KRW) was measured on an eight-point scale (1 = no income; 2 = below 500,000 won [approximately $459 USD as of June 2015]; 3 = 500,000–1 million [approximately $917 USD]; 4 = 1 million–2 million [approximately $1834 USD]; 5 = 2 million–3 million [approximately $2751 USD]; 6 = 3 million–4 million [approximately $3668 USD]; 7 = 4 million–5 million [approximately $4585 USD]; and 8 = above 5 million).
3.3. Participants As shown in Table 1, the majority of the 3053 participants were in their 30s (20.0%) and 40s (18.4%). There were more female (57.6%) than male (42.4%) participants. In terms of education, most respondents reported having graduated from high school (31.7%), followed by having graduated college (30.5%). For monthly income, people with no income accounted for 50.7%, which represented more than half of the total number of participants. This was followed by participants who reported their incomes in the ranges of 1–2 million (15.1%) and 2–3 million (11.5%). The reason that there was a high percentage of participants without any income stems from the high percentage of participants in the under 20 (18.6%) and over 60 (21.5%) ages groups, both of which reflect low economic participation. Demographic factors may have some influences on the time spent on old video media such as TV, or the time spent on other activities. Age has been found to be positively associated with TV viewing (Kaye and Johnson, 2003; Bondad-Brown et al., 2012), but negatively associated with other activities, such as Internet usage (Bondad-Brown et al., 2012), and physical
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Table 2 The number of media users and the average time spent per day (N = 3053). Media
2010
2011
2012
Number of users
Average time spent per day Number of (SD) users
Average time spent per day Number of (SD) users
Average time spent per day (SD)
226
68.9 (113.2)
199
71.6 (90.7)
194
51.6 (60.5)
Video media Terrestrial TV Cable TV IPTV Satellite TV DMB
788 2092 213 127 62
178.1 (144.8) 208.0 (158.9) 159.8 (131.5) 177.8 (164.4) 50.5 (63.1)
693 2151 238 112 69
173.9 (136.3) 212.8 (146.5) 180.6 (136.7) 136.9 (124.2) 55.4 (69.0)
832 1984 312 108 50
151.5 (117.3) 205.0 (139.6) 167.3 (110.5) 138.4 (155.9) 33.9 (26.1)
Non-video media Radio Newspaper Game Social media
334 739 540 150
100.7 (133.9) 39.0 (40.8) 72.7 (76.4) 43.4 (57.5)
317 748 550 166
88.8 45.6 63.9 49.8
298 620 532 191
87.3 41.4 60.9 60.0
3053
1078.8 (216.7)
3053
1041.1 (40.5)
3053
1055.7 (193.3)
Online video services
Non-media activities
(125.6) (60.1) (55.6) (61.4)
(106.4) (46.0) (59.9) (84.6)
leisure activities (Mota and Esculcas, 2002; Pate et al., 1994). Income may negatively affect video media usage (Gorely et al., 2004; Sidney et al., 1996; Reid et al., 1980). Education may also negatively affect the time spent on video media (Sidney et al., 1996). In existing literature, gender has been generally not found to be significantly associated with video media usage, however, Mierlo and Bulck (2004) and Kahlor and Eastin (2011) found that women watched more video media than men. Men have been found to be more involved in other activities such as leisure activities, compared to women (Mota and Esculcas, 2002; Gallagher et al., 2012). Table 2 shows the number of participants using online video services was 226 in 2010, 199 in 2011, and 194 in 2012. The average number of minutes spent on online video services per day was 68.9 (standard deviation [SD] = 113.2); 71.6 (SD = 90.7); and 51.6 (SD = 60.5), respectively. During the three-year study period, the number of participants who watched online video services was higher than the number of participants who used satellite TV, DMB, and social media; conversely, this number was smaller than the number of participants who used terrestrial TV, cable TV, IPTV, radio, and games. The average time each user spent on online video services per day was less than half of the average time spent on terrestrial TV, cable TV, or IPTV per day. Cable TV had the highest number of users among all media: 2092 in 2010; 2151 in 2011; and 1984 in 2012. The average number of minutes these users spent on cable TV per day was 208.0 (SD = 158.9) in 2010; 212.8 (SD = 146.5) in 2011; and 205.0 (SD = 139.6) in 2012; this equated to the highest number of minutes among all of the media considered. The average number of minutes spent on non-media activities per day was 1078.8 (SD = 216.7) in 2010; 1041.1 (SD = 40.5) in 2011; and 1055.7 (SD = 193.3) in 2012, which equates to less than 18 h per day. 3.4. Statistical analysis The main objective of this study is to examine the effect of online video services viewing on both old media use and nonmedia activities. As we explained previously, we used panel data, which asked survey participants to record the media they used from 2010 to 2012. In this study, we calculated average values for three observations for each variable, and used these average values to obtain consistent media usage patterns for each observation. In order to test the effect of online video services viewing on both old media use and non-media activities, we employed the seemingly unrelated regression (SUR) model.1
4. Results Table 3 summarizes the statistical results. The coefficients presented in this table represent regression coefficients. As previous research has suggested, age positively affected video media usage, but negatively affected other activities, such as non-video media activities and non-media activities. Income negatively affected the time spent on video media. In terms of gender, it was found that women used video media more, but less involved other activities, than men. 1 The errors in a set of related regression equations are often correlated, and the efficiency of the estimates can be improved by taking these correlations into account (Cameron and Trivedi, 2005; Greene, 2000). The efficient estimator for the SUR model is generalized least squares (Zellner, 1962; Greene, 2000). Zellner’s study (1962) showed that regression coefficients, which were estimated simultaneously by applying Aitken’s generalized least-squares to the whole system, were more efficient than classical single-equation least-squares estimators. The SUR model fully relaxes the constraints on the coefficients, and assumes that each equation has its own fixed set of parameters.
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Model 1 revealed the effect of online video services viewing on both video media use, such as terrestrial TV, cable TV, IPTV, satellite TV, and DMB, and other activities that include non-video media use and non-media activities. The result indicated that the time spent on online video services negatively affected both the time spent on video media (b = .25, p < .01) and the time spent on other activities (b = .94, p < .01). This suggests that as people spend more time on watching online video services, they reduce both the usage of video media and participation in other activities. In Model 2, we subdivided old media into two parts, video media and non-video media, and examined the effect of the time spent on online video services on three dependent variables (video media, non-video media, and non-media activities). The results indicated that the time spent on online video services negatively affected both the time spent on video media (b = .197, p < .05) and non-media activities (b = .94, p < .01), but it did not affect the time spent on non-video media (b = .004, p > .05) (RQ1 and RQ2). Roughly, a one-minute increase in online video viewing reduces approximately .2 min in video media consumption and .9 min from other activities. Model 1 and Model 2 indicated that online video services viewing not only replaced old video media, but also became a part of the viewer’s activities by reducing non-media activities. Considering the sizes of the coefficients in Model 1 and Model 2, this finding implies an increase in the total time spent on media consumption. Interestingly, the effect of online video services on non-media activities was found to be greater than that of online video services on old video media.2 Model 3 subdivided online video services viewing into three categories (TV program viewing, movie viewing, and UGC viewing) and examined which types of content affected video media usage, non-video media usage, and non-media activities. The results indicated that watching movies provided by online video services negatively affected video media usage (b = .283, p < .05) (RQ3). Conversely, watching TV programs (b = 1.32, p < .01) and movies (b = .778, p < .01) provided by online video services negatively affected non-media activities (RQ4).
5. Conclusion This study examined whether online video services are displacing old media (video and non-video media) in terms of time. It also verified whether the use of online video services decreased the time spent on non-media activities; i.e., whether it increased the total time spent on all media (total media consumption) (Neuman et al., 2012). The results related to RQ1 indicated that the time spent on online video services negatively affected the time spent on old media in the sample observed. Specifically, the time spent on online video services negatively affected the time spent on old video media, but it did not affect the time spent on old non-video activities. The results are in line with those found by Kayany and Yelsma (2000) and Lee and Kuo (2002), who argued that Internet media has served to decrease the amount of time households spent on watching TV. From an industry perspective, these results suggest that online video services are now starting to form a competitive relationship with old video media such as TV, and could displace such media in the future, although online video services have not decreased the number of cable TV subscribers in South Korea as of yet. The results that users reduced the time spent on old video media such as TV to use online video services indicate their transition from push media to pull media (Neuman et al., 2012). Users used online video services—which can be accessed anywhere and anytime, and provide video content that they want to watch—instead of old video media, which can only be accessed at home and provide only the content chosen by service providers. In other words, consumption of video content is gradually changing in a manner that provides greater control to consumers. The results related to RQ3 revealed that among the various content available, watching movies provided by online video services decreased the time spent on old video media. In other words, the reason why online video services have a time displacement effect on old video media was that the more time users spent watching movies provided by online video services, the less they spent on old video media. Conversely, watching TV programs or UGC provided by online video services did not affect the time spent on old video media. These results indicated that displacement and complementary relationships can differ according to the types of content provided by a new medium, as argued by Dutta-Bergman (2004). Regarding RQ2 and RQ4, the time spent on online video services negatively affected the time spent on non-media activities in the sample. This suggests that in order to make time for online video services, people decrease not only the time they spend on old video media but also the time they spend on non-media activities (Himmelweit et al., 1958; Mutz et al., 1993; Weiss, 1969). Therefore, this result revealed that the time spent on online video services increased the total media consumption. Meanwhile, online video services decreased the time spent on non-media activities more than the time spent on old video media. This suggests that people decrease the time they spend on non-media activities rather than old video media to make time to view online video services. The reason why online video services decreased the time spent on non-media activities was that watching TV programs and movies provided by online video services decreased the time spent on non-media activities. In other words, watching TV programs and movies through online video services increases the total media consumption. These results explain one of the reasons behind Neuman et al.’s (2012) findings, which suggested that the total media consumption of American households was gradually increasing. With the emergence of new media such as online video services, users reduce the time they spent on non-media activities to make time to use the new media. 2 According to Chi-square test, the coefficient of online video services viewing on old video media use was statistically different from that of online video services viewing on non-media activities.
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Table 3 SUR results (N = 3053). Model 1 Video media Online video Online video (TV) Online video (movie) Online video (UGC) Age Income Education Gender R2 Obs. * **
.25 (.094)**
Model 2 Other activities Video media .94 (.116)**
.197 (.099)*
Model 3 Non-video media Non-media .004 (.046)
Video media .004 (.186)
.004 (.086)
1.32 (.235)**
.283 (.12)*
.009 (.056)
.778 (.151)**
.018 (1.1) .939 (.116)** 6.53 (.186)** 2.03 (.214)** .026 (.23) .764 (.293)** 3.87 (.364)** 6.25 (.774)** 7.67 (.959)** .36 .19 3053 3053
5.19 (.99)* 1.9 (.227)** .378 (.31) 4.08 (.818)** .23 3053
.86 (.091)** .066 (.105) .653 (.144)** 3.29 (.381)** .07 3053
Non-video media Non-media
.94 (.125)**
.934 (.125)** .093 (.286) 4.52 (.391)** 4.37 (1.03)** .13 3053
.374 (.374)
5.18 (.196)** .858 (.091)** 1.91 (.227)** .067 (.105) 3.89 (.31) .65 (.144)** ** 4.05 (.818) 3.29 (.381)** .24 .07 3053 3053
.344 (.1399) 4.22 (.247)** .118 (.286) 4.51 (.391)** 4.31 (1.03)** .13 3053
Significant at 5%. Significant at 1%.
Conversely, due to time (Kayany and Yelsma, 2000; Mutz et al., 1993) and attention (Neuman et al., 2012) constraints, it is expected that the total time users spend on all media will not continue to increase. Neuman and associates also determined that the discrepancy between the total media supply and the total media consumption increased as the years passed because the total media supply was increasing exponentially, while the total media consumption was increasing linearly and modestly (Neuman et al., 2012). As there might be no technical limit on the supply of media (Neuman et al., 2012), it could increase exponentially. However, as there are only 24 h in a day, media consumption cannot increase dramatically. With the appearance of a new medium, people consume part of the time that would have been spent on non-media activities. However, they do not have unlimited time to use a new medium. There are a few limitations to this study. First, the media diary survey was conducted on only Korean participants, so it is unclear as to whether our findings could be generalized to other countries. By controlling the impact of a national background, further studies may collect data from different countries to generalize our results. Second, this study utilized secondary data that were collected by KISDI, thus there was a limitation in conducting an in-depth analysis. For example, this study classified the types of content as TV programs, movies, and UGC. However, for an in-depth analysis on the media displacement effect that online video services have on old media, it would be helpful to classify content into genres such as drama, news, documentary, comedy, education, and others (Cha, 2013). Third, the results from the media diary survey suggested that the actual amount of time spent on a certain media could have been overestimated if the users spent less time than 15 min on that media, as the respondents were asked to record the media use every 15 min. Finally, this study failed to consider various aspects of online video services such as the content linearity, devices to use the services, and pricing. Future research should consider other various aspects of online video services to examine the time displacement effect of that media. References Adomavicius, G., Tuzhilin, A., 2005. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17 (6), 734–749. Bondad-Brown, B.A., Rice, R.E., Pearce, K.E., 2012. Influences on TV viewing and online user-shared video use: demographics, generations, contextual age, media use, motivations, and audience activity. J. Broadcast. Electron. Media 56 (4), 471–493. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge. Cha, J., 2013. Does genre type influence choice of video platform? A study of college student use of internet and television for specific video genres. Telematics Inform. 30 (3), 189–200. Cha, J., Chan-Olmsted, S.M., 2012. Substitutability between online video platforms and television. Journal. Mass Commun. Q. 89 (2), 261–278. Chang, C.C., 2013. Examining users’ intention to continue using social network games: a flow experience perspective. Telematics Inform. 30 (4), 311–321. Cirucci, A.M., 2013. First person paparazzi: why social media should be studied more like video games. Telematics Inform. 30 (1), 47–59. Coffin, T., 1955. Television’s impact on society. Am. Psychol. 10 (10), 630–641. ComScore, 2012. ComScore releases July 2012 U.S. online video rankings. 2012. 8. 9. Dimmick, J.W., 2003. Media Competition and Coexistence: The Theory of the Niche. Lawrence Erlbaum Associates, Mahwah, NJ. Dimmick, J.W., Kline, S., Stafford, L., 2000. The gratification niches of personal e-mail and the telephone: competition, displacement, and complementarity. Commun. Res. 27 (2), 227–248. Dutta-Bergman, M.J., 2004. Complementarity in consumption of news types across traditional and new media. J. Broadcast. Electron. Media 48 (1), 41–60. FCC, 2012. Annual assessment of the status of competition in the market for the delivery of video programming. MB Docket No. 07-269, FCC 12-81. FCC, 2013. Annual assessment of the status of competition in the market for the delivery of video programming. MB Docket No. 12-203, FCC 13-99. FCC, 2015. Annual assessment of the status of competition in the market for the delivery of video programming. MB Docket No. 14-16, FCC 15-41. Gallagher, P., Yancy Jr., W.S., Swartout, K., Denissen, J.J.A., Kuhnel, A., Voils, C.I., 2012. Age and sex differences in prospective effects of health goals and motivations on daily leisure-time physical activity. Prev. Med. 55, 322–324. Gorely, T., Marshall, S.J., Biddle, S.J.H., 2004. Couch kids: correlates of television viewing among youth. Int. J. Behav. Med. 11 (3), 152–163. Greene, W., 2000. Econometric Analysis. Prentice Hall, NJ. Himmelweit, H.T., Oppenheim, A.N., Vince, P., 1958. Television and the Child. Oxford University Press, London.
S.Y. Lee et al. / Telematics and Informatics 33 (2016) 247–255
255
James, M.L., Wotring, C.E., Forrest, E.J., 1995. An exploratory study of the perceived benefits of electronic bulletin board use and their impact on other communication activities. J. Broadcast. Electron. Media 39, 30–50. Kahlor, L., Eastin, M.S., 2011. Television’s role in the culture of violence toward women: a study of television viewing and the cultivation of rape myth acceptance in the United States. J. Broadcast. Electron. Media 55 (2), 215–231. Kang, M., Atkin, D.J., 1999. Exploring the role of media uses and gratifications in multimedia cable adoption. Telematics Inform. 16, 59–74. Kaplan, S.J., 1978. The impact of cable television services on the use of competing media. J. Broadcast. 22, 155–165. Kayany, J.M., Yelsma, P., 2000. Displacement effects of online media in the socio-technical contexts of households. J. Broadcast. Electron. Media 44 (2), 215– 229. Kaye, B.K., Johnson, T.J., 2003. From here to obscurity?: Media substitution theory and traditional media in an on-line world. J. Am. Soc. Inform. Sci. Technol. 54 (3), 260–273. KISDI STAT Homepage