Why do Chinese young adults media multitask with smartphones?
A study on the motivations for smartphone media multitasking
Xiaoxia Chen 12078328 Masters’ Thesis
Graduate School of Communication Master’s programme Communication Science
Supervisor: Dr. I. Rodriguez de Dios 28th of January, 2020
Abstract
Media multitasking, especially media multitasking with smartphones, is a prevalent media behavior among young adults. Despite a large amount of studies have investigated the motivations behind media multitasking, little research has specifically focused on
smartphones. In order to fill this gap, this study examined three predictors of the smartphone media-media multitasking (SMMT): motivations, demographic characteristics, and total smartphone usage. To investigate the different motivations for different smartphone
multitasking types, we categorized SMMT by forms (e.g., TV-based vs. SNS-based vs. instant message-based multitasking) and contents (e.g., task-relevant multitasking vs. task-irrelevant multitasking). An online survey among 200 Chinese young adults whose ages ranging from 18 to 30 was conducted. The results showed that habit was the most relevant motivation for SMMT, which predicted general and all subtypes of SMMT, except task-irrelevant
multitasking. Furthermore, the motivation of habit can mediate the relationship between age and general SMMT. Last, habit, information, efficiency, and enjoyment are also correlated to some certain smartphone multitasking types.
Keywords: Smartphone media multitasking; age; gratifications; Chinese young adults Introduction
Media multitasking has been increasingly popular among people in recent years. Researchers have defined media multitasking as simultaneously engaging in media
consumption and other activities (Baumgartner, Weeda, Van Der Heijden, & Huizinga, 2014). With the development of technology, smartphones have become one of the most dominant sources for various media content, on which most media consumption can be done, e.g.
reading eBooks, watching TV, playing electronic games, and listening to music (Yeykelis, Cummings, & Reeves, 2014). In 2018, it was reported that 2.9 billion smartphone users worldwide (Statista, 2019a), and Hootsuite and We Are Social (2019) showed that almost half the time that people spent online was on smartphones. Along with the popularity of
smartphone use, media multitasking with smartphones has also become a dominant media behavior of smartphone users (Lim & Shim, 2016). For a typical media day, people can use social media on their phones and watch TV at the same time, and make a phone call while playing electronic games. They can even engage in single-device media multitasking through using only the smartphone. For instance, a survey showed that 72% of adults would check social media while watching TV on their phones in the United States in 2017 (Statista, 2019b). However, as far as we know, although a large amount of research on general media
multitasking exist (e.g., the effects of media multitasking: Baumgartner et al., 2014; Magen, 2017; Ophir, Clifford Nass, & Anthony D, 2009; the motivations of media multitasking: Hwang, Kim, & Jeong, 2014; Jeong & Fishbein, 2007; Kononova & Chiang, 2015; Lin, 2019; Wang & Tchernev, 2012; Yang & Zhu, 2016), little research on smartphone media
multitasking has been conducted. Therefore, this study will only focus on media multitasking with smartphones as it is a novelty topic.
Media multitasking is a very broad concept, which includes media-nonmedia multitasking and media-media multitasking in usual (Yang & Zhu, 2016). Media-media multitasking refers to engaging in two or more media tasks at the same time, while media-nonmedia multitasking focusing on using media while doing real-life tasks
great significance. Practically, with the number of media multitaskers increasing, the media landscape has changed as well. A large number of people, especially of young generations, are no longer content with consuming just one medium at a time (Chang, 2017). Faced with this phenomenon, the media content producers, advertisers, employers and educators all need to make corresponding strategies, and the first step is to understand why people want to engage in media multitasking (Zhang & Zhang, 2012).
Previous research also revealed that media multitasking can negatively affect people’s cognitive processes. For instance, Ophir et al. (2009) found that heavy media multitaskers did worse at filtering out distracting information than light media multitaskers. Moreover, media multitasking was found to be negatively correlated with working memory and attentional control (Baumgartner et al., 2014; Magen, 2017).
Smartphone multitasking may also induce similar effects. Because of technology development, nowadays, smartphones generally function as a palm computer, which enables individuals conveniently multitask in different settings (Grinols & Rajesh, 2014). When multitasking with smartphones in the classroom or workplace, the productivity of learning or working would be affected due to various distraction sources, targets, and subjects on the smartphone (Chen & Yan, 2016; Grinols & Rajesh, 2014). However, as the influence of media multitasking varies based on its different types, it is crucial to figure out the motivations behind different types of smartphone multitasking (Hwang et al., 2014).
Theoretically, as far as we know, there is abundant research on the impacts of media multitasking, but the research on the motives behind media multitasking, especially the research on the smartphone-based multitasking, just started in recent years. Lim and Shim
(2016) examined different motives behind three types of smartphone multitasking (e.g., nonmedia multitasking with smartphones, cross-media multitasking with smartphones and single-device multitasking) through the uses and gratifications theory. However, there is little research only focusing on media-media multitasking on smartphones which compares the motives between different forms (e.g., TV, social media, instant message) and different content types (e.g., task-relevance versus task-irrelevance).
Previous research found that the difference in smartphone ownership and media usage habits between different countries could influence the motivation of media multitasking (Kononova & Chiang, 2015). For instance, American media users were found more likely to be engaged in media multitask for cognitive and entertainment needs, while Taiwanese respondents had more addiction motivations for media multitasking. China now has 817 million mobile Internet users (CNNIC, 2019) and has more people accessing the Internet via mobile phone than via computers since 2014 (Statista, 2019c), so investigating this topic in China has a great research significance. However, there is little research about the motives for mobile-based media multitasking among Chinese people.
Furthermore, the report from Pew Research Center (2019) showed that adults aging between 18 and 29 relied more on smartphones to get access to the Internet than other age groups, and younger adults were also found to prefer to engage in media multitasking (Hwang et al., 2014). Therefore, it is reasonable to propose that young adults are the most frequent smartphone media-media multitaskers. Compared to other age groups, young adults have various characteristics (Arnett, 2006) and specific media needs (Coyne, Padilla-Walker, & Howard, 2013), which could explain their multitasking with smartphones. From this
perspective, the current study will only focuses on young adults, aiming to explore their motivations for the smartphone media-media multitasking (SMMT).
Therefore, this study aims to figure out different motivations for different types of media-media multitasking with smartphones through the uses and gratifications theory, and will fill the following two research gaps: (1) This study only focuses on the smartphone-based media-media multitasking, and explores its motivations; (2) The target population of this study is Chinese young adults aged 18 to 30, which has not been investigated sufficiently yet.
Theory Background
SMMT refers to using the smartphone and engaging in other media activities
concurrently (Lim & Shim, 2016; Yang, & Zhu, 2016), or switching different media activities between each other frequently on a single smartphone (Lim & Shim, 2016; Yeykelis,
Cummings, & Reeves, 2014). When performing media-media multitasking on smartphones, there can be different pairs of media activities, such as watching TV while sending messages, and checking SNS while listening to music. Also, some multitaskers would prefer to consume related media contents simultaneously while others prefer irrelated ones. Therefore, this study would investigate the motivations of different types multitasking.
Motivations for general SMMT
The uses and gratifications theory proposes that “the social and psychological origins of needs, which generate expectations of the mass media or other sources, which lead to
differential patterns of media exposure (or engagement in other activities), resulting in need gratifications and some other consequences” (Katz, Blumler, & Gurevitch, 1973, p510). This theory has been widely applied to the investigation of the motivations behind different media
usage, such as television usage (Rubin, 1983), smartphone usage (Hiniker, Patel, Kohno, & Kientz, 2016; Joo & Sang, 2013), Internet usage (Luo, Chea, & Chen, 2011; Stafford, Stafford, & Schkade, 2004), and also media multitasking (Hwang et al., 2014; Lim & Shim, 2016; Yang & Zhu, 2016; Zhang & Zhang, 2012).
Various motivations were identified by previous studies. There were two main purposes of media use: instrumental and ritualistic purposes (Rubin, 1984). Instrumental media use is purposeful and refers to people searching and consuming certain media content to gratify their information-seeking needs, while ritualistic media use is more for nonutility needs and is habitual (Rubin, 1984). For smartphone use, Hiniker et al. (2016) revealed that when
smartphone users contacted others, looked up information, tracked their health or fitness, used maps, or used other utilities via smartphones, they were seeking instrumental gratifications. On the other hand, when they wanted to gratify their ritualistic needs, they would check social media, play games, scan or read the news via smartphones. Individuals were found to spend more time on ritualistic media use than instrumental media use in daily life (Hiniker et al., 2016; Joo & Sang, 2013)
Scholars also identified a series of specific gratifications for Internet use. Stafford et al. (2004) proposed three main gratifications for Internet use, they were “process gratification”, “content gratification”, and “social gratification”. Process gratification refers to individuals searching for information online for a certain purpose or just random browsing. Content gratification is the need for consuming specific media content. Both process and content gratifications have already been found in traditional media use (e.g., TV), while social gratification was specifically for Internet use, referring to the need of building and
maintaining the social relationship. Luo et al. (2011) also emphasized the social gratification for Internet use, and proposed that this is the reason for the popularity of SNS.
People have various ways to engage in media multitasking, such as combining traditional media use (e.g., print media, TV, audio) and new media use (e.g., Internet, computer,
smartphone). Therefore, the motivations for media multitasking are more varied than using a single medium. In addition to the gratifications mentioned before (e.g., instrumental, habitual, information seeking, entertainment, social), the control and the efficiency are another two important motivations for media multitasking. The interviewees of Bardhi, Rohm, and Sultan (2010) thought that media multitasking benefited them in four aspects. First, they can have a better sense of control as they can engage in all the media they are interested in. Second, using several media concurrently can be more effective as they can use less time to gain more information. Third, it was more enjoyable for them to multitask than using a single medium. Last but not least, the interviewees thought that when engaging in media multitasking, they could have more connections with their friends and families as well as cultural surroundings. Therefore, the control, efficiency, entertainment, and interaction were the underlying
motivations for media multitasking, which were proved in following studies. Focusing on computer-based media multitasking, Zhang and Zhang (2012) found that the general computer multitasking was strongly related to the control gratification, the habitual gratification, and the social gratification. Hwang et al. (2014) conducted an online survey among Korean adults, and the results showed that the need for information seeking, the need to manage time efficiently, and the habitual factor were positively correlated with general media multitasking as well. In Kononova and Chiang (2015), the five motivations, including
control, entertainment, connection, efficiency and habit, were all found to be correlated with media multitasking.
As the smartphone is a tool for performing multiple media activities, the motivations for smartphone multitasking are similar with the motivations mentioned before. Lim and Shim (2016) examined three motivation factors for smartphone multitasking: efficiency, utility (e.g., useful for work/study/spare time), and positive affect (e.g., feeling fun, mood modification), finding that the general smartphone multitasking can be predicted by efficiency and positive affect.
According to the studies mentioned above, we can synthesize the five most substantial motivations for media multitasking: (1) seeking information, (2) social factors, (3) efficiency, (4) entertainment, (5) habitual factors. In terms of using smartphones, individuals have great freedom to download the applications they want and choose which to use actively (Hiniker et al., 2016), which corresponds to the core assumption of the uses and gratifications theory that media users are goal-oriented. However, due to smartphone’s nature of mobility, individuals can multitask with the smartphone in more situations than with a computer. Therefore, when SMMT is the sole target of the research, there must be some specific motivations that do not exist in other types media multitasking or computer-based multitasking, so the first research question is:
RQ1: What are the motivations for general media-media multitasking with smartphones among Chinese young adults?
Age, gender, smartphone usage, and SMMT
younger adults use more frequently Facebook and post more contents than older adults (Quan-Haase & Young, 2010). This could be explained by the study from Malik, Dhir, and Nieminen (2016) that younger adults have less social influence gratifications for SNS use than older adults. Generating SNS content is like a routine for younger adults, while older adults would consider more social influence of generating and sharing SNS contents. Researchers also thought that younger adults and older adults had different motivations for Internet use. Younger adults used Internet mainly to seek social, entertainment, and academic gratifications, while older adults generally aimed to seek for serious information (Dhir & Torsheim, 2016). Age differences were also been found in media multitasking. Younger adolescents multitasked more in cross-media and mobile multitasking than older adolescents (Zhang & Zhang, 2012), while those who aging between 17 and 24 years old were found to engage less in media multitasking than both younger and older age groups (Voorveld & van Der Goot, 2013). Thus, the role of age in motivations for media multitasking is unclear yet and we cannot formulate a hypothesis with a clear direction. However, it is reasonable to assume that the motivations for media multitasking are influenced by age. With relevance to our study, we can formulate the research question and hypothesis as:
RQ2: To what extent are motivations of general SMMT correlated with age?
H1: There will be a relationship between age and general SMMT, and this relationship
will be mediated by motivations via information, social, efficiency, enjoyment, and habit.
Previous literature showed that females and males have different gratifications for media use, and when the media was different, their motivations would be different as well. For instance, when it comes to mobile chat, Nysveen, Pedersen, and Thorbjørnsen (2005) found
that the influence of entertainment gratifications on females’ intention to use mobile chat service were stronger than males’. However, when it comes to mobile TV use, the results were different. Female’s attitudes towards watching mobile TV was specifically predicted by social interaction, while males’ by the gratification of entertainment and fashion (Kyun Choi, Kim, & Mcmillan, 2009). Moreover, other studies also revealed that males more tended to seek for the gratifications of disclosure, habit, “likes and comments”, and “gain popularity” than females when using SNS (Dhir & Torsheim, 2016; Malik, Dhir, & Nieminen, 2016). In terms of the direct relationship between gender and media multitasking, although two experiments showed that women were better than men at multitasking (Stoet, O’connor, Conner, & Laws, 2013), there were some different findings. Some scholars found that females were more frequently engaged in media multitasking than males (Foehr, 2006; Hwang et al., 2014), while other studies did not find any relationship between gender and smartphone multitasking (Lim & Shim, 2016; Zhang & Zhang, 2012). Since the findings in previous studies were different, we cannot propose a detailed hypothesis. Therefore, this study would investigate the role of gender in SMMT, and formulate the followed research question and hypothesis:
RQ3: To what extent are motivations of general SMMT correlated with gender? H2: There will be a relationship between gender and general SMMT, and this
relationship will be mediated by motivations via information, social, efficiency, enjoyment, and habit.
In addition to the factors mentioned before, the total smartphone usage could also be a predictor of SMMT. In Lim and Shim (2016)’ s study, the media users who spend more time
on smartphones were more frequently engaged in single-device multitasking than others. Thus, we can formulate the followed hypothesis to investigate the relationship between total
smartphone use and SMMT:
H3: The amount of total smartphone use will positively correlate with general SMMT.
Motivations for different forms of SMMT
When engaging in media multitasking, there can be different combinations. In a pioneering study of media multitasking, Ophir, Clifford, and Anthony (2009) developed a
Media Use Questionnaire to measure media multitasking, and it has been widely used in the following studies. In this questionnaire, 12 media items which were thought to be the most popular media forms were included (e.g.,print media, television, computer-based video, music, nonmusical audio, electronic games, voice calls, instant message, SMS, email, website, and other computer-based applications). However, as the media landscape changes, some media forms are not used frequently. Therefore, Baumgartner, Lemmens, Weeda, and Huizinga (2017) developed a short media multitasking measure based on Ophir et al. (2009) and found that the most popular media multitasking combinations nowadays were using media while listening to music, watching TV, sending messages, and using SNS.
The motivations for different types of media multitasking may be different. Based on two observational studies, the most common multitasking behavior while watching TV was using the smartphone (Rigby et al., 2017; Shokrpour & Darnell, 2017). Even when
individuals had other activities to do, they used to keep the TV open. It is already a habit to open the TV when at home (Shokrpour & Darnell, 2017). What’s more, researchers also
individuals were used to gathering in front of the TV, but focused on other activities (Rigby et al., 2017). Thus, the first motivation for TV-related multitasking can be habitual gratification (Hwang et al., 2014). Zhang and Zhang (2012) also found that the affective gratifications predicted the multimedia type of computer media multitasking, which can be proposed that individuals engaging in TV and other media simultaneously could also aim to seek more fun (Dias, 2016).
The use of social network sites (SNS) has a great prevalence among young adults. The main motivation for SNS is to gratify social and affective needs (Leung, 2013). When individuals generate contents on SNS, they are expressing their ideas, feelings, and interests to their families and friends. This process can strengthen individuals’ social ties and satisfy their social needs. Even when individuals are just browsing SNS without generating any content, they can also feel a lot of fun from it. What’s more, nowadays, SNS has also become a place for individuals to gain news and search information, which means that seeking
information has become another motivation for using SNS (Quan-Haase & Young, 2010). There is an important similarity between the motivations for using instant messaging and those for using SNS: they are all for passing time (Quan-Haase & Young, 2010). However, as instant message is a more private tool, the main motivation for using it is to maintain the social relationship (Leung, 2001). The motivations mentioned before can also be applied to SNS-related and instant message-related multitasking.
Therefore, to figure out the motivations for specific multitasking forms (e.g., TV-based, SNS-based, instant message-based), we form the research question as:
adults?
Motivations for different contents of SMMT
The task relevance is another research direction for media multitasking. Researchers defined task-relevant multitasking as engaging in two related media tasks at the same time, and task-irrelevant multitasking as being involved in two unrelated media tasks (Dias, 2016).When individuals engage in task-relevant multitasking, their cognitive demands are thought to be lower than using several completely irrelevant media. Researchers thus proposed that they would have higher cognitive responses and better recall than those who engaged in task-irrelevant multitasking, and even than those who used a single medium. Although the experiments turned out that there was no difference in the cognitive effects between task-relevant and task-irrelevant multitasking (Kazakova, Cauberghe, Hudders, & Labyt, 2016; Ran & Yamamoto, 2019; Van Cauwenberge, Schaap, & van Roy, 2014), their motivations were showed to be different.
Shokrpour and Darnell (2017) interviewed their participants and foundthat when they watched TV, they would search unknown words or other information showed on TV, and send related messages or generate related social media content at the same time. In this case, the motivations can meet their information, social, and entertainment gratifications
(Shokrpour & Darnell, 2017). In the exploratory study of Dias (2016), the participants who engaged in task-relevance media multitasking were reported to had a specific gratification called “instantaneity addiction”, which means that they wanted to know everything in real-time. However, to our knowledge, the difference in motivations between task-relevance and task-irrelevance media multitasking has not been tested in quantitative research yet.
Therefore, we formulate the following research question to further test this phenomenon:
RQ5: What are the motivations for different contents of SMMT among Chinese young adults?
Method Participants
We conducted a cross-sectional online survey among 205 Chinese. Respondents for this study were recruited from the researcher’s social network. We sent the URL of the survey to the respondents and invited them to complete it. To make the respondents have a better and easier understanding of the questionnaire, we translated it to Chinese in the back-forward translation method and both languages (i.e., Chinese and English) were included. Five participants were dropped from the study because their ages were not between 18 to 30 or missing more than 3 questions, making the total sample size 200. 32.49% of the respondents were males and 67.51% were females, while three respondents did not provide their sex. The mean age was 21.74 (SD = 2.69).
Measures
The questionnaire included measures of general SMMT, form-specific SMMT, content-specific SMMT, and gratifications. Sociodemographic variables and smartphone usage were also measured in this questionnaire.
Form-specific and general SMMT. This study adapted the scale generated by Baumgartner et al. (2017) to measure the frequency of SMMT. Watching TV/video, using SNS, and sending instant messages were found to be the most prevalent primary activities of media multitasking, while listening to music was thought to be a background media activity
and only to be used as a secondary activity in this scale. Therefore, there were three subscales for each of the three primary media activities. In each of the subscale, participants were asked how frequently they engaged in the specific media on smartphones with each of the other two primary media activities and listening to music at the same time. The devices of the secondary activities were not limited. Thus, each subscales included three items. For instance, in the first subscale, participants were asked, “While watching TV/video on a smartphone, how often do you engage in listening to music / using SNS / sending instant messages”. 5-point Likert scales from never (= 1) to always (= 5) were used as the response options. For each of the subscale, an index was created to measure the specific forms of smartphone multitasking by calculating the mean of the three items. Therefore, there were three categories of
form-specific SMMT, they were “TV-based multitasking” (M = 3.20, SD = 1.07, α = .74), “SNS-based multitasking” (M = 3.43, SD = .86, α = .55), and “Instant message-based multitasking” (M = 3.20, SD = .90, α = .60). Furthermore, an overall index was created to measure the general SMMT by calculating the average of the nine items (M = 3.28, SD = .81,
α = .84).
Content-specific SMMT. There were two genres of content-specific SMMT in this study, they were task-relevant multitasking (M = 3.08, SD = .90) and task-irrelevant
multitasking (M = 3.06, SD = .91). To measure task-relevant multitasking, respondents were asked, “While using a smartphone and doing other media activities at the same time, how often are the contents of the two tasks connected?”. Meanwhile, a reverse question was asked to measure task-irrelevant multitasking, “While using a smartphone and doing other media activities at the same time, how often are the contents of the two tasks distracted?”. The
response options for the two questions were 5-point Likert scales from never (= 1) to always (= 5).
Motivations. This study adopted the scale developed by Hwang, Kim, and Jeong (2014). Respondents were asked, “To what extent do you engage in media-media multitasking with smartphones due to each of the following reasons?”, and there were 20 motives items, such as “to seek additional information”, “to express my opinion”, “to save time”, and so on. The response options were 5-point Likert scales from not at all (= 1) to very much (= 5). According to Hwang, Kim, and Jeong (2014), the 20 items can be categorized as 5 factors, “information” (M = 3.42, SD = .83, α = .82), “social” (M = 3.24, SD = .85, α = .83),
“efficiency” (M = 3.09, SD = .98, α = .86), “enjoyment” (M = 3.03, SD = 1.09, α = .83), and “habit” (M = 2.92, SD = .98, α = .71).
Sociodemographic variables and smartphone usage. Respondents were asked to provide their age and gender at the end of survey. Meanwhile, to measure the amount of total smartphone use, respondents were asked to fill out the average time of using a smartphone for the past week in hours, and they were suggested to check the applications which can
automatically record their smartphone usage (M = 6.55, SD = 2.77).
Results Motivations for general SMMT
Research question 1 asked what were the motivations of general SMMT. To answer this question, a multiple regression model was used to test the relationship between general
smartphone multitasking and five motivations (e.g., information, social, efficiency, enjoyment, and habit). The regression model was significant, F(5, 194) = 10.38, p < .001. The predictors
in this model explain 21% of the variance in general smartphone multitasking (R2 = .21). The regression model can therefore moderately predict the general smartphone multitasking behavior. The gratifications of information, b* = .16 , t = 2.14, p = .034, 95% CI [.01, .30], and habit, b* = .29 , t = 3.47, p = .001, 95% CI [.11, .38], were weakly associated with the general smartphone multitasking. Meanwhile, all the association were positive. For each additional point on the scale of information, the frequency of the general smartphone media multitasking would increase .16, and for each additional point on the scale of habit, the frequency of multitasking would increase .24. Social, b = -.01 , b* = -.01 , t = -.10, p = .923, 95% CI [-.18, .17], efficiency, b =.06, b* = .07, t = .91, p = .366, 95% CI [-.07, .18], and enjoyment, b = .07, b* = .07, t =1.06, p =.293, 95% CI [-.06, .20], were not significantly correlated with general smartphone multitasking. Therefore, the research question 1 can be answered: information and habit motivations predict general SMMT.
The mediation role of motivations in the relationship between age and general SMMT Research question 2 investigated the relationship between age and the motivations of general SMMT. Hypothesis 1 assumed that age would be correlated to the general SMMT, and this relationship would be mediated by motivations. Thus, this study employed Hayes’ PROCESS SPSS macro (Model 4; 5,000 bootstraps) to test this mediated relationship. The results showed that age was positively correlated with social (b = .07, p = .001), enjoyment (b
= .07, p = .010), and habit (b = .08, p = .003), but had no direct relationship with general SMMT (see Figure 1).
However, according to Table 1, there was a positive significant indirect relationship that older individuals more frequently engaged in general SMMT through a higher habit
motivation (95% CI [.00, .04]). This represented a relatively small effect. The mediated effects of information, social, efficiency, and enjoyment were not significant.
Figure 1. Coefficients for the relationship between age and general multitasking (N= 200)
Table 1
Indirect effects of age on general SMMT:
Mediator Coefficients 95% CI Information .01 [-.00, .01] Social -.00 [-.01, .01] Efficiency .00 [-.00, .01] Enjoyment .01 [-.00, .02] Habit .02 [.00, .04]
The mediation role of motivations in the relationship between gender and general SMMT
Research question 3 investigated the relationship between gender and the motivations of general SMMT. Hypothesis 2 assumed that gender would be correlated to the general SMMT, and this relationship would be mediated by motivations. Thus, we employed another Hayes’ PROCESS SPSS macro (Model 4; 5,000 bootstraps) to test this mediated relationship, and
b = .24, p < .001 b = .07, p = .010 b = .07, p = .296 Direct effect, b = .01, p = .750 b = .04, p = .109 b = .06, p = .366 b = .16, p = .034 b = -.01, p = .894 b = .03, p = .117 b = .07, p = .001 Age Information Social Efficiency Enjoyment Habit General SMMT b = .08, p = .003
gender was coded as “0 = female”, “1 = male”. The results showed that there was no significant direct effect of gender on either motivations and general SMMT (see figure 2). The indirect relationships between gender and multitasking via information (95% CI
[-.03, .08]), social (95% CI [-.02, .02]), efficiency (95% CI [-.02, .04]), enjoyment (95% CI
[-.04, .03]), and habit (95% CI [-.12, .04]) were also not significant in this study (see table 2).
Figure 2. Coefficients for the relationship between gender and general multitasking (N = 197)
Table 2
Indirect effects of gender on general SMMT:
Mediator Coefficients 95% CI Information .02 [-.03, .08] Social -.00 [-.02, .02] Efficiency .01 [-.02, .04] Enjoyment -.00 [-.04, .03] Habit -.03 [-.12, .04] b = .25, p < .001 b = -.13, p = .378 b = -.07, p = .653 b = .07, p = .363 Direct effect, b = .03, p = .773 b = .19, p = .295 b = .06, p = .297 b = .20, p = .008 b = -.03, p = .742 b = .09, p = .474 b = .01, p = .958 Gender Information Social Efficiency Enjoyment Habit General SMMT
Total smartphone usage and general SMMT
Hypothesis 3 posited that the amount of total smartphone use would positively be correlated with general SMMT. The regression model with total smartphone usage as independent variable, and general SMMT as a dependent variable was employed to test this hypothesis. The regression model was significant, F(1, 187) = 9.66, p < .001. The predictor in
this model explains 5% of the variance in general smartphone multitasking (R2 = .05). The regression model can therefore weakly predict the general smartphone multitasking behavior. The amount of total smartphone use, b* = .22 , t = 3.11, p = .002, 95% CI [.02, .10], can weakly predict the general smartphone multitasking. Meanwhile, respondents who spent one more hour to use a smartphone would increase .06 points general smartphone media
multitasking (b = .06).
Motivations for form-specific SMMT
To answer the research question 4 that what are the motivations for different SMMT forms, three regression models were used (see table 3). The regression model with the
TV-based smartphone multitasking as a dependent variable was significant, F(5, 194) = 6.35, p < .001. The predictors in this model explained 14% of the variance in TV-related
smartphone multitasking (R2 = .14). Thus, the TV-based smartphone multitasking behavior can be moderately predicted. The gratifications of efficiency, b* = .18, t = 2.33, p = .021, 95% CI [.03, .32], and habit, b* = .26, t = 3.00, p = .003, 95% CI [.10, .48], were weakly associated with the general smartphone multitasking. Respondents with one more point gratifications of efficiency would increase .20 points TV-based smartphone multitasking, and respondents with one more point gratifications of habit would increase .29 points TV-based smartphone
multitasking. Information, b = .07 , t = .69, p = .494, 95% CI [-.13, .27], social, b =.06, t = .46,
p = .650, 95% CI [-.18, .29], enjoyment, b = -.08, t = -.89, p =.375, 95% CI [-.26, .10] were
not significantly correlated with TV-related smartphone multitasking.
The regression model with the SNS-based smartphone multitasking as a dependent
variable was significant, F(5, 194) = 8.99, p < .001. The predictors in this model explained 19%
of the variance in SNS-related smartphone multitasking (R2 = .19). The regression model can therefore moderately predict the SNS-based smartphone multitasking behavior. The
gratifications of habit, b* = .27 , t = 3.18, p = .002, 95% CI [.09, .39], was weakly associated with the SNS-based smartphone multitasking. .24 points of SNS-related smartphone
multitasking would increase if respondents had one more point gratifications of habit.
Information, b = .14 , t = 1.82, p = .070, 95% CI [-.01, .30], social, b = .01 , t = .14, p = .888, 95% CI [-.17, .20], efficiency, b =.01, t = .07, p = .945, 95% CI [-.13, .13], enjoyment, b = .10,
t = 1.37, p =.171, 95% CI [-.04, .24], were not significantly correlated with SNS-based
smartphone multitasking.
The regression model with the instant message-based smartphone multitasking as a dependent variable was the best model among the three regression models, with 20% of the variance in instant message-related smartphone multitasking can be explained (R2 = .20), F(5, 194) = 9.82, p < .001. The gratifications of information, b* = .24 , t = 3.16, p = .002, 95% CI
[.10, .42], enjoyment, b* = .23, t = 2.62, p = .010, 95% CI [.05, .35], and habit, b* = .22, t = 2.57, p = .011, 95% CI [.05, .36], were weakly associated with the instant message-based smartphone multitasking. Respondents with one more point gratifications of information would increase .26 points instant message-based smartphone multitasking; one more point of
enjoyment or habit would both increase .20 points instant message-based smartphone
multitasking. Social, b = -.10 , t = -1.04, p = .298, 95% CI [-.30, .09], and efficiency, b =.03, t = -.49, p = .624, 95% CI [-.17, .10] were not significantly correlated with instant
message-related smartphone multitasking. Table 3
Regression models to predict form-specific and content-specific SMMT (N = 199)
Form-specific multitasking Content-specific multitasking
TV-based SNS- based Instant message- based Task-relevant Task-irrelevant b* b* b* b* b* Information .05 .14 .24** .15 -.06 Social .04 .01 -.19 .18 -.01 Efficiency .18* .01 -.04 -.03 .07 Enjoyment -.08 .12 .23* .07 -.08 Habit .27** .27** .22* .17* -.03 R2 .14 .19 .20 .19 .01 F 6.35*** 8.99*** 9.82*** 8.90*** .42 Adjusted R2 .12 .17 .18 .17 -.05
Note. * p < .05. ** p < .01. *** p < .001. The α of SNS-based multitasking (α = .55) and instant
message-based multitasking (α = .60) is low, which might influence the accuracy of the result.
Motivations for content-specific SMMT
Another two multiple regressions were assessed to answer research question 5 that what are the motivations for different SMMT content (see table 3). The regression model with the task-relevant smartphone multitasking as dependent variable was significant, F(5, 194) = 8.90, p < .001. The predictors explained 19% of the variance in task-relevant smartphone
multitasking (R2 = .19). However, the regression model with the task-irrelevant smartphone multitasking as the dependent variable was not significant, F(5, 194) = .42, p = .833, R2 = .01.
Therefore, only the task-relevant smartphone multitasking can be predicted. The gratification of habit, b = .16, b* = .17 , t = 2.02, p = .045, 95% CI [.00, .32], was weakly associated with the task-relevant smartphone multitasking. Respondents who have more gratifications of habit would perform more task-relevant smartphone multitasking. Information, b = .16, t = 1.93, p = .056, 95% CI [-.00, .32], social, b = .19, t = 1.96, p = .052, 95% CI [-.00, .39], efficiency, b = -.03, t =.46, p =.649, 95% CI [-.17, .10], and enjoyment, b = .06 , t = .83, p = .410, 95% CI [-.09, .21] were not significantly correlated with task-relevant smartphone multitasking.
Discussion
This study examined the relationships between personal motivations, characteristics, and SMMT among Chinese young adults. Five motivations (e.g., information, social, efficiency, enjoyment, and habit), three form-specific multitasking types (e.g., TV-based, SNS-based, instant message-based), and two content-specific multitasking types (e.g., task-relevant, task-irrelevant) were identified. The results showed that different motivations of SMMT predict different multitasking types (see table 3). There was also a relationship between the personal characteristics and general SMMT.
In terms of the general SMMT, information and habit were two significant predictors of it. Due to the development of technology, the smartphone was no longer a device only for making phone calls, but a mobile palm computer with multiple functions (Hwang et al., 2014). The changing nature of smartphones made users have lots of access to various information at the same time. Thus, those who have more motivation for seeking information would engage more in SMMT. Furthermore, in the study of Kononova and Chiang (2015), Americans tended to multitask in media for entertainment and efficiency motivation, while Taiwanese
media multitaskers were motivated by habit. The Chinese respondents of this study also reported the same results that those who thought media-media multitasking with smartphones as a routine would engage more in the multitasking.
In terms of the form-specific SMMT, we found that the motivation of habit also
predicted all the three types of multitasking. This aligned with the exploratory research from
Robinson (2017) that habit was an underlying motive for media multitasking. The
interviewees of Robinson (2017) reported that keeping the TV turning on was already a habit that people just need the background noises when they engage in the other media, and they usually used SNS or sent instant messages unconsciously. Some of them even reported that they could not stop media multitasking and were addicted to it. Other empirical research also indicated the same results (Hwang et al., 2014; Kononova & Chiang, 2015; Lim & Shim, 2016; Zhang & Zhang, 2012). Therefore, we can conclude that habit is a crucial motivation for SMMT among Chinese young adults.
Apart from habit, TV-based multitasking was also predicted by efficiency, which can be explained in the perspective that individuals thought it is useful to manage time efficiently (Hwang et al., 2014). When individuals used the smartphone watching TV or video, it would take them a longer time than using other media. However, TV no longer commands full attentions of users. They can save time and gain more information and feelings
simultaneously by engaging in multitasking (Van Cauwenberge et al., 2014).
Instant message-based multitasking was predicted by habit, information, and enjoyment. As mentioned before, the habit motivation for instant message-based multitasking was due to the unconscious addiction of smartphone chatting. What’s more, when individuals used the
smartphone to send instant messages and did other media activities at the same time, they were searching and exchanging the information (Kononova & Chiang, 2015), and it was not for social motivation. Another motivation for instant message-based multitasking was enjoyment. The reason for this result can be that sending instant messages to others was thought to be enjoyed, and entertained (Nysveen et al., 2005). Thus, Chinese young adults who have more motivation for habit, information, or enjoyment would more frequently engage in instant message-based multitasking.
The results of the content-specific multitasking were out of our expectation. Habit was the only predictor of task-relevant multitasking, while there were no significant predictors of task-irrelevant multitasking. As a previous study showed, participants who had more
information, social, and entertainment gratifications would engage more in task-relevant multitasking than others (Shokrpour & Darnell, 2017). The different findings may be due to the different measures. The literature we mentioned before mainly conducted interviews or experiments, while our study used the survey method, which may lead to the
misunderstanding of the questions (Bryman, 2016).
Besides, there was also relationships between personal characteristics and general SMMT. First, age can predict general smartphone multitasking only when the motivation of habit was influenced by age. Older individuals would have more habit motivation than younger individuals, resulting in multitasking more with smartphones. This finding was in line with previous research that age positively predicted the media multitasking (Voorveld & van Der Goot, 2013; Yang & Zhu, 2016). However, as far as we know, the mediation role of the habit was a novel finding, which has not been investigated directly. This result can be
explained by the motivation and cognitive control theory that as age grows, individuals’ cognitive processing, emotional goals, and motivations would change as well. Older individuals prefer more habitual motivations than goal-oriented motivations for decision making, while younger individuals perform the opposite (Braver, 2016). Therefore, the motivation of habit for SMMT increases in age, and predict more multitasking behavior eventually.
We didn’t find any significant relationship between gender, motivation and general SMMT. It is consistent with recent research that gender cannot predict both computer multitasking (Zhang & Zhang, 2012) and smartphone multitasking (Lim & Shim, 2016). Gender also did not correlate with any motivations, which was different from the previous research (Dhir & Torsheim, 2016; Kyun Choi et al, 2009; Malik et al., 2016; Nysveen et al., 2005). This result can be influenced by the unbalanced gender ratio of the sample.
The amount of smartphone use was a significant predictor of general SMMT, which was in line with previous smartphone multitasking research (Lim & Shim, 2016). Thus, we can propose that with the prevalence of smartphone usage, SMMT will be increasingly popular among Chinese young adults.
Implications
This study has three theoretical contributions. First, we explored the motivations for SMMT among Chinese young adults, which was a gap that has not been investigated thoroughly. This provides a good explanation of the reason why smartphone media
multitasking is prevalent among Chinese young adults. Second, although the motivations for different types have been investigated a lot recently, there was little comparation between
different multitasking forms and different content (e.g., task-relevant vs task-irrelevant). This study fills this gap and enhance the application of U&G in media multitasking. Last but not least, it provides empirical evidence that the smartphone multitasking’s motivations differs in age. The mediated relationship between age, motivation, and smartphone multitasking
enriches the media multitasking’s motivation model.
Furthermore, this study has a number of practical implications as well. Previous research showed that media multitasking had a series of negative effects on users’ cognitions, such as information recall, learning, and attention (Baumgartner et al. 2014; Magen, 2017; Ophir et al., 2009). However, the findings of the specific SMMT motivation can be helpful. For instance, the advertisers should realize that habit is the most important motivation for Chinese young adults. They do not multitask for a certain reason in most of the time, and multitasking with smartphones is regarded as a routine for them. Thus, it is important to create more situations for multitasking, such as provide other background noise for them when they were watching advertisement, or create several interact tasks on different platforms to increase their interests of the advertisement, or make them access information through various way simultaneously as information is also a crucial motivation for them.
Limitations and future studies
There are also some limitations that need to be noticed, and the results thus should be approached with caution. First, the Cronbach’s alpha for SNS-based multitasking (α = .55) and instant message-based (α = .60) is a little bit low, which represents questionable
reliabilities of these two factors. As reviewed before, the nationality and age could influence the media multitasking behaviors. Therefore, the reason may be the different cultural
backgrounds and ages of our sample. The media multitasking scale we used in this study was developed by Baumgartner et al. (2017). They developed and tested the scale based on an adolescent sample from the Netherlands’ high schools, while the respondents of this study were Chinese young adults. Another reason for the low reliability could be the adaption of the scale. Even we used a back-forward method to translate the scale to Chinese, there could also be some misunderstanding for respondents. With low reliability, the factor may not measure what the research indeed wants to measure (Bryman, 2016). Therefore, it is an important concern for future studies that they should develop a more reliable scale for smartphone media multitasking and Chinese respondents.
Second, in the media multitasking scale, there were only four media activities involved. For each media activity (except music), we asked respondents to indicate the frequencies of multitasking with this specific activity on a smartphone and other three activities. Although each item (except music) were accessed twice, for both primary activity and secondary activity, the respondents can distinguish the difference between primary activities and
secondary activities (Baumgartner et al. 2017). Therefore, it was reasonable to categorize the smartphone media multitasking types by different primary activities. However, except for the three smartphone media multitasking forms, other types such as media multitasking when reading, or playing video games also have scientific and practical relevance to investigate. Thus, in future studies, more media items should be involved and using the factor analysis to make the categories more distinguishable.
Third, the results of our study have difficulties in generalization due to the convenience sample. Also, with the unbalanced gender ratio, we did not find any gender difference in
motivations and smartphone media multitasking. Future studies should try to use proportional sampling and access to more respondents. Furthermore, future studies can further test the moderate role of gender in the relationship between motivations and smartphone media multitasking.
Conclusion
In summary, age, total smartphone usage, and motivations are positively correlated with SMMT. The role of habit motivation is highlighted in this study. First, habit motivation is the most prevalent motivation of SMMT, which can predict general, all form-specific, and task-relevant SMMT. Second, the relationship between age and general SMMT is also
mediated by habit motivation. Moreover, efficiency can predict TV- based multitasking, while both information and enjoyment can predict the instant message-based multitasking. The amount of total smartphone usage also predict general smartphone media multitasking.
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Appendix Form-specific and general SMMT scale (9 items) 1 = Never, 5 = Always
1. While watching TV/video on smartphone, how often do you engage in the following activities:
当你用手机看电视/视频时，你同时进行以下活动的频率是多少: listening to music 听音乐
sending messages 发消息
using social networking sites 使用社交网站/软件
2. While using social network sites on smartphone, how often do you engage in the following activities:
当你用手机使用社交网站/软件时，你同时进行以下活动的频率是多少: listening to music 听音乐
sending messages 发消息 watching TV/video 看电视/视频
3. While sending messages via smartphone, how often do you engage in the following activities:
当你用手机发消息时，你同时进行以下活动的频率是多少: listening to music 听音乐
using social networking sites 使用社交网站/软件 watching TV/video 看电视/视频
SMMT’s gratifications scale (20 items) 1 = Not at all, 5 = Very much
To what extent do you engage in media-media multitasking with smartphones due to each of the following reasons? 你在多大程度上是由于以下原因才进行智能手机进行多媒体任务 处理的？
1. To seek additional information 为了寻求额外的信息 To resolve curiosity 为了满足好奇心
To check facts 为了验证事实
To gain more information about product or services 为了获得更多关于这个产品或服务的 信息
To look up unfamiliar words or people 为了查找不熟悉的词或人 To express my opinion 为了表达我的观点
To feel a sense of belonging to a group 为了获得一种归属感 To maintain (interpersonal) relationship 为了维持人际关系 To share opinions with others 为了和他人分享观点
To save time 为了节省时间
To manage time efficiently 为了更高效率的管理时间 Because I have little time 因为我没有时间
Because multitasking is efficient 因为同时使用几个媒体是高效率的 Because multitasking is fun 因为同时使用几个媒体是很有趣的 Because multitasking is enjoyable 因为我很享受同时使用几个媒体 Because it is boring to use a single medium 因为只用一个媒体很无聊 Because multitasking is a habit 因为同时使用几个媒体是我的习惯 To pass time 为了消磨时间
