Mobile information seeking and political participation: A differential gains approach with offline and online discussion attributes

Abstract

This study, derived from a differential gains model, examines how mobile-based political information seeking is associated with offline and online political participation in interaction with three political discussion features: frequency, size, and heterogeneity. Data from a Web survey of an online panel indicate that the link between mobile information seeking and offline and online political participation is greater for respondents who discuss politics with others face to face and online more frequently and a greater diversity of others face to face and online.

Keywords

Information seeking mobile phones political discussion political participation

Mobile phones have become a necessity for Americans. As of April 2015, 92% of American adults owned a cell phone, and as of July 2015, 68% of them owned a smartphone device (Anderson, 2015). Communication facilitated by mobile phones, including smartphones that offer advanced computer capabilities, is characterized by such concepts as omnipresence, ubiquity, immediacy, portability, and mobility (Campbell and Park, 2008; Ishii, 2006; Katz and Aakhus, 2002; Martin, 2014; Schrock, 2015). Supported by wide broadband network coverage, users can engage in a versatile range of activities. For example, people can use mobile phones to communicate immediately with others in distant locations through calling, texting, emailing, and social networking sites. They can also go online via mobile phones to enjoy multimedia content for entertainment and seek information about life events (Anderson, 2015; Campbell and Kwak, 2010; Smith, 2015).

Given these characteristics, mobile phones present new opportunities for political campaigns and engagement. In the 2016 US presidential election, candidates, such as Donald Trump and Hillary Clinton, used text messages and mobile apps to deliver the latest news, connect supporters with campaigns, and provide volunteer opportunities, among others. Mobile phones also facilitate unique political experiences for individual citizens. For example, information seeking can be more efficient with mobile phones. Users can search political news and information easily through search engines, news Websites, and mobile apps at home away from a desktop or laptop computer, while walking on the street, while socializing with friends, and so forth. Users can also efficiently perform expressive activities with mobile phones. They may share a news article or personal opinions on political issues through text messages, social media, and emails, while posting photos or videos they have taken with their mobile devices.

The growing presence of mobile phones and applications in politics has generated concerted efforts to understand the role of mobile-based communication activities in citizen engagement (Campbell and Kwak, 2010, 2011; Chen and Givens, 2013; Martin, 2013, 2014, 2015; Rojas and Puig-i-Abril, 2009; Yamamoto et al., 2015). For example, using mobile phones for news and information is related to monetary contributions to a relief effort (Martin, 2013), civic engagement (Campbell and Kwak, 2010; Rojas and Puig-i-Abril, 2009), and political participation (Campbell and Kwak, 2011; Martin, 2015; Yamamoto et al., 2015).

The goal of this study is to extend this line of research. Moving beyond independent effects of informational use of mobile phones, we assess a joint, interactive linkage of mobile political information seeking and political discussion in predicting offline and online political participation. In doing so, we consider three aspects of political discussion, namely, frequency, size, and heterogeneity. The central expectation tested in this study is derived from the differential gains model (Scheufele, 2002). To test this expectation, we analyze data collected through a Web survey of a national online panel. Understanding the political utility of mobile-based information seeking complemented by interpersonal discussion activities will add to the literature and possibly inform political campaigns of ways to develop mobile communication strategies and tactics.

Mobile use and political participation

Mobile-based communication uniquely differs from other forms of communication in important respects. The pervasive ownership of mobile phones, their portable nature, and wide network coverage enable immediate, continuous, and on-the-go styles of communication (Ling, 2008). Unlike face-to-face communication, mobile communication is not restricted by physical distance. Users can contact a select individual directly and immediately through calling, texting, emailing, and social networking sites, which creates a sense of constant connections with others (Katz and Aakhus, 2002). Also, unlike communication with other digital devices such as desktop and laptop computers, mobile phones enable continuous human communication and engagement in online activities while users physically move from one place to another (Ling, 2008) and in places where use of a larger device is inconvenient or considered awkward (Schrock, 2015).

These characteristics of mobile communication enhance people’s capacity to perform political communication activities. Using mobile phones, citizens can seek information about politics virtually whenever they become curious or want to know about a political issue, event, and candidate by entering a search query, accessing news apps, or visiting news Websites, and wherever as long as the use of mobile phones is permitted and they can access the Internet at a reasonable speed. Then, they can share and talk about political news and information they have learned with others in close proximity or online social networks. The versatile nature of mobile phones, coupled with dynamic Web functions, allows performance of information seeking and engagement with information simultaneously or in rapid succession, both offline and online.

An emerging body of research has reported that mobile-based political information consumption has the potential to enrich civic and political life. For example, Rojas and Puig-i-Abril (2009) examined how the informational use of mobile phones would be related to offline civic participation. They showed that phone use for news and information was positively related to online expressive activities, which in turn were linked with increases in politically mobilizing use of social networking sites and mobile phones, which led to civic participation. Martin (2015) found that mobile news use predicted the increased likelihood of having voted in an election and mobile device use to make campaign donations. Yamamoto et al. (2015) also showed that using news and political campaign apps was positively related to offline political participation.

A differential gains model of political participation

While mobile-based political information seeking may predict civic and political participation, its utility may be further enhanced in combination with other communication activities, one of which is interpersonal political discussion. A theoretical grounding for this expectation is based on the differential gains model put forth by Scheufele (2002). The model theorizes the role of political discussion in moderating the effects of news media use on political participation, such that the effects are stronger for those who often discuss politics with others.

The differential gains model is based on two assumptions. First, discussing what is learned from news sources helps solidify one’s orientation to public affairs (Scheufele, 2002). When individuals talk with others about public affairs news, they undertake multiple cognitive activities. They think more elaborately about public affairs than simply receiving news without further involvement with it. Discussing political news and information with others helps people learn new information and perspectives from interactants, clarify their thoughts as they attempt to clearly describe and explain political issues to others, and address areas of confusion, leading to a clearer and deeper understanding of politics (Kim and Kim, 2008; Kim et al., 1999; Scheufele, 2002). Furthermore, political discussion fosters stronger identification with public affairs as people bring their personal experiences and situations to discussion of public matters, which would allow them to make sense of political information and further acquire useful information for political action (Kim and Kim, 2008).

Second, based on a uses-and-gratifications perspective, discussion helps increase the motivation to pay attention to and comprehend news information. Individuals who often talk with others about public affairs likely anticipate future discussions. They may need or want to make logical arguments to persuade others, defend their positions, and counter opposing views (Scheufele, 2002). Such anticipation would induce careful processing of news. As individuals systematically process news information, they can gain useful information for political action.

Prior research has shown empirical support for the differential gains model (Hardy and Scheufele, 2005; Nisbet and Scheufele, 2004; Scheufele, 2002). For example, Scheufele (2002) reported that the relationship newspaper and TV hard news use, respectively, had with political participation was stronger for citizens who discussed public affairs with others more frequently. This moderating role of discussion is not limited to face-to-face settings. As an extension of the model, Hardy and Scheufele (2005) showed that the relationship between Internet hard news use and political participation was stronger for those who chatted online about politics more often.

Although the existing evidence supports the role of political discussion frequency in moderating the effects of news media use in political participation, the literature indicates that discussion is not only about how frequently or infrequently one talks with others about issues of concern. Discussion is characterized by other related yet different attributes such as the size and heterogeneity of a discussion network (Kwak et al., 2005; Scheufele et al., 2004, 2006). This study, based on the existing literature, considers three aspects of discussion to examine whether political discussion enhances the political utility of mobile information seeking.

Discussion network features

The literature on political communication indicates that political discussion is characterized by multiple features. The most common dimension of political discussion is frequency examined in terms of how frequently or infrequently one talks about politics with others. Research has consistently supported the value of frequently talking about politics with others, with, for example, those who often discuss politics with others reporting high levels of political knowledge, efficacy, and participation (Jung et al., 2011; Nah et al., 2006; Shah et al., 2005).

While variations in political discussion frequency have implications for political participation, discussion has other dimensions as well, one of which is the size of a discussion network. Size refers to the number of people with whom one talks (Eveland and Hively, 2009; Kwak et al., 2005; Nah et al., 2006). The size of a discussion network is distinct from discussion frequency and can be examined in its own right, as frequency of discussion does not necessarily translate into network size. That is, people who discuss subjects frequently may or may not talk to a large number of individuals. It is entirely likely that people engage in discussion often, yet only talk with a limited number of people. The value of discussion network size lies in the affordances of a network. A large discussion network enables group members to connect with others who are interested in public affairs and who could serve as a source of political information, messages, and possibly motivation that the members might otherwise not be able to get on their own (Boyd, 2011; Eveland and Hively, 2009; Verba et al., 1995). In fact, research has shown a positive relationship between discussion network size and political outcomes (Eveland and Hively, 2009; Gil de Zúñiga and Valenzuela, 2011; Kwak et al., 2005).

Yet another important aspect of discussion identified in the existing literature is the heterogeneity of a discussion network, which refers to the diversity of people with whom one talks (Scheufele et al., 2004). This dimension of discussion differs from discussion frequency and network size, as those who discuss topics of interest frequently may do so with others who share a common background. Similarly, the size of a network does not tell how heterogeneous the network is. In fact, research shows that one’s discussion network tends to be homogeneous, given people’s tendency to interact with similar, like-minded others (McPherson et al., 2001), such as liberals interacting with others with the same political orientation (Bakshy et al., 2015). Research has shown that network heterogeneity is positively related to political participation (Scheufele et al., 2004, 2006). Those who belong to a heterogeneous discussion network tend to encounter different viewpoints and disagreements, which help increase information seeking, careful information processing, and reasoning and solidify engagement with politics (Scheufele et al., 2004).

It is important to point out that the three dimensions of political discussion reviewed above are not limited to face-to-face, offline settings. Political discussion can occur in online settings as well. Online platforms, such as social networking sites, microblogging, and content-sharing sites, enable individuals who are geographically separated to build and maintain social connections. Thus, even if people cannot discuss politics face to face with each other, they can choose to do so on the Internet. Online platforms also enable individuals to be more expressive than they might be offline because of such factors as a lack of direct social cues, asynchronous interaction, and anonymity. Thus, individuals who avoid discussing politics face to face might discuss politics online more frequently or have a larger and more diverse discussion network. As one may or may not talk about politics with the same individuals across offline and online settings, it is important to simultaneously consider not only offline discussion network attributes but also corresponding online counterparts (Gil de Zúñiga and Valenzuela, 2011; Gil de Zúñiga et al., 2012; Nah et al., 2006; Valenzuela et al., 2012).

Goals of the study

The main hypothesis, derived from the differential gains model, is that mobile-based political information seeking will have a stronger relationship with offline and online political participation for individuals who talk about politics with others more frequently. The portable, immediate, and versatile nature of mobile phones, coupled with the ubiquitous presence of high-speed wireless Internet, enables mobile users to search information effectively almost anywhere, anytime, including information about politics.

Such mobile information seeking is important in its own right, yet can be even more meaningful if people talk about what they have learned through information seeking behavior. Doing so helps people more effectively learn about and engage with politics. The capacities of mobile phones to facilitate seamless, on-the-go engagement between information seeking and political discussion are unparalleled (e.g. Campbell and Kwak, 2010, 2011; Martin, 2015). For example, after reading political news via mobile phones, individuals can talk about politics with someone face to face or on social media while walking outside. Doing so is not convenient with larger devices. Mobile phones also allow people who frequently discuss politics to easily search for political information almost anywhere, anytime. Although mobile phone use might lower the quality of a conversation by detracting interactants’ attention from it (Ling, 2008; Przybylski and Weinstein, 2013), mobile phones at least offer political opportunities that are uniquely different from other forms of communication. As noted earlier, we consider three discussion features: frequency, size, and heterogeneity. In doing so, we simultaneously take into account offline discussion frequency, size, and heterogeneity and each corresponding online discussion feature, as it is plausible that these discussion features are not the same across offline and online settings.

H1. Mobile information seeking will have a stronger association with offline political participation for those who discuss politics with others (a) face to face and (b) online more frequently.

H2. Mobile information seeking will have a stronger association with offline political participation for those who discuss politics with a larger number of others (a) face to face and (b) online.

H3. Mobile information seeking will have a stronger association with offline political participation for those who discuss politics with a greater diversity of others (a) face to face and (b) online.

H4. Mobile information seeking will have a stronger association with online political participation for those who discuss politics with others (a) face to face and (b) online more frequently.

H5. Mobile information seeking will have a stronger association with online political participation for those who discuss politics with a larger number of others (a) face to face and (b) online.

H6. Mobile information seeking will have a stronger association with online political participation for those who discuss politics with a greater diversity of others (a) face to face and (b) online.

Method

Data came from an online panel recruited by Survey Sampling International (SSI). Online panels consist of prescreened individuals who volunteer or opt-in to participate in research projects in return for incentives. A Web survey was conducted online between May and June 2015, with 6048 invites distributed to panel members. A total of 1201 participants completed the survey, with a final response rate of 19.86%. Although the use of an opt-in online panel is becoming increasingly common in recent years, a sample collected in this way cannot be viewed as representative of a larger population, in the present case the general American public, because every member of the population does not have an equal chance of being included in a sample. In a similar vein, it is possible that members of online panels have higher levels of digital media use through mobile phones and the Internet (Kees et al., 2017), which might lead to overestimation of the role of mobile media in politics. Therefore, caution should be taken in interpreting results.

Measurement

Offline political participation

In total, seven items were used to measure offline political participation (e.g. Gil de Zúñiga et al., 2012). Respondents were asked whether they did each of the following in the past 2 years (0 = no; 1 = yes): attend a civic forum or meeting where citizens spoke about local issues; contact a local newspaper, television station, or radio station; sign a petition for a local candidate or issue; contact a local public official; attend any local rallies, protests, boycotts, or marches; vote in a local election; and work for a political campaign locally. Responses were summed to form an additive index (M = 1.89, standard deviation [SD] = 1.93, α = .79).

Online political participation

In total, four items were used to measure online political participation (Kaufhold et al., 2010). Respondents were asked, on a 7-point scale (1 = not at all; 7 = very often), how often in the past 2 years they did each of the following activities using the Internet: contact, contribute to, or sign up to follow a politician; volunteer for a campaign/issue; email a political message; and write a letter to the editor of a newspaper, radio, or television. Responses were averaged to form a 7-point scale (M = 2.69, SD = 1.94, α = .95).

Mobile information seeking

In total, two items were used to measure mobile information seeking. Respondents were asked how often they used mobile phones to search for information online about national politics or international affairs and about local politics or community issues on a 7-point scale (1 = never; 7 = frequently). Responses were averaged on a 7-point scale (M = 3.34, SD = 2.14, r = .93).

Offline and online discussion frequency

In total, two sets of four items were employed to measure offline and online political discussion frequency (e.g. Kwak et al., 2005; Shah et al., 2005). Respondents were asked, on a 7-point scale (1 = not at all; 7 = very frequently), how often they talked about politics or current issues face to face (M = 3.94, SD = 1.66, α = .84) and online (M = 3.06, SD = 1.93, α = .92) with family members, neighbors, co-workers, and other friends and acquaintances.

Offline and online discussion network size

A single item was used to measure offline and online discussion network size, respectively (e.g. Gil de Zúñiga and Valenzuela, 2011). Respondents were asked, over the last month, about how many people they had talked about politics or current issues face to face (M = 6.26, SD = 13.21) and via the Internet such as emails, Facebook, Twitter, or bulletin boards (M = 6.61, SD = 15.94). Both variables were not normally distributed with positive skewness and high kurtosis values (offline discussion network size: skewness = 5.20, kurtosis = 31.23; online discussion network size: skewness = 4.13, kurtosis = 18.85). Thus, log transformation was applied prior to analysis to normalize the distribution of offline discussion network size (M = 1.35, SD = 1.02, skewness = .61, kurtosis = .39) and online discussion network size (M = 1.05, SD = 1.23, skewness = 1.01, kurtosis = .15), respectively.

Offline and online discussion network heterogeneity

We used two sets of seven items to measure offline and online network heterogeneity (Scheufele et al., 2004, 2006). Respondents were first asked, on a 7-point scale (1 = not at all; 7 = very frequently), how often they talked about politics or current issues face to face and online with people of a different gender, people with extreme right-wing views, people with extreme left-wing views, people who are Democrats, people who are Republicans, people of a different race or ethnicity, and people belonging to a different religion.

The items tapping discussion with people with extreme right-wing views and with extreme left-wing views were recoded following Scheufele et al.’s (2004) coding scheme. We first created a measure of political ideology using two items. Respondents were asked to rate on a 7-point scale (1 = very liberal; 7 = very conservative) their stand on economic issues and social issues, respectively. Responses were summed to create an additive scale. Respondents who scored in the top 5% of this scale were assigned 0 for the items measuring discussion with extreme right-wing views (face to face and online, respectively), and those who scored in the bottom 5% of this scale were assigned 0 for the items measuring discussion with extreme left-wing views. The assumption is that respondents who were on extreme ends of this scale are not likely to gain much from discussion with other people who had similar ideological viewpoints (Scheufele et al., 2004). For the items tapping discussion with people who were Democrats and Republicans, we used a question asking respondents whether they considered themselves to be a Democrat, Republican, Independent, or other. Those who identified themselves as Democrats were assigned 0 for the items measuring discussion with people who were Democrats (face to face and online, respectively), and those who identified themselves as Republicans were assigned 0 for the items measuring discussion with people who were Republicans. The remaining items were used as they were, as they directly measured heterogeneity. After the above recoding, all items were combined to form an additive index offline (M = 3.31, SD = 1.50, α = .85) and online (M = 2.77, SD = 1.74, α = .92).

Statistical controls

Several variables were introduced as statistical controls to counteract potential misspecification errors, including age (M = 42.42, SD = 13.16), gender (female = 39.5%), education (median = 5, Associate’s degree), income (median = 6, US$50,000–US$74,999), and race (White = 43.1%). Political interest was measured by a single 7-point scale item, asking respondents how interested they were in politics (M = 4.53, SD = 1.78). Political ideology, as noted above, was measured by two items asking about respondents’ stances on political and economic issues, respectively. Responses were averaged on a 7-point scale (M = 3.90, SD = 1.58, r = .90).

Traditional media use was measured by eight items tapping exposure and attention to television news and newspaper stories about national politics or international affairs and local politics or community issues. Responses were combined and averaged on a 7-point scale (M = 4.34, SD = 1.67, α = .94). Online news use was measured by four items tapping exposure and attention to Internet news stories about national politics or international affairs and local politics or community issues. Responses were averaged on a 7-point scale (M = 4.76, SD = 1.75, α = .94).

Analytic strategy

The hypotheses were tested using SPSS PROCESS macro (Hayes, 2013). Offline and online political participation were regressed on control variables, mobile information seeking, offline and online forms of political discussion, and interaction terms between mobile information seeking, and an offline discussion feature and its online counterpart (model 1). We estimated regression models separately with a pair of offline and corresponding online discussion features to examine whether they would differentially moderate the relationship between mobile information seeking and political participation. Significant interaction effects were further investigated at the mean and 1 SD below and above the mean of a moderating variable. Estimated values of offline and online participation were requested to plot significant interaction effects.

Results

H1 predicted that mobile information seeking would have a stronger association with offline political participation for those who discuss politics with others (a) face to face and (b) online more frequently. This hypothesis was supported. The first column in Table 1 shows that the interaction term between mobile information seeking and offline discussion frequency had a significant association with offline political participation (B = .03, p < .01). As shown in Table 2, the association between mobile information seeking and offline political participation was significant and positive when frequency of face-to-face political discussion was high (effect = .086, confidence intervals [CIs]: [.010, .163]), but was not significant when it was low (effect = −.045, CIs: [−.133, .044]) and moderate (effect = .021, CIs: [−.046, .088]). Figure 1 shows this interaction pattern.

Table 1.

An interactive effect of mobile information seeking and offline and online discussion features on offline political participation.

	Offline political participation
	Discussion frequency		Network size		Network heterogeneity
Age	.00*	.01*	.00*	.00*	.01**	.01**
Gender	−.30**	−.30**	−.27**	−.27**	−.26**	−.27**
Education	.10**	.10**	.09*	.08*	.11**	.10**
Income	−.01	−.01	.00	−.00	−.01	−.01
Race	.02	.02	−.00	−.01	.03	.03
Political ideology	−.03	−.03	−.00	−.00	−.03	−.02
Political interest	.22***	.22***	.19***	.19***	.22***	.21***
Offline discussion feature	.13**	.11*	.20***	.19**	.10	.07
Online discussion feature	.10*	.11**	.38***	.38***	.18***	.20***
Traditional news use	.14**	.13**	.18***	.18***	.16***	.15***
Online news use	.01	.01	−.04	−.04	.00	.00
Mobile information seeking	.02	.03	.05	.05	.01	.02
Mobile × offline discussion feature	.03**	–	.00	–	.06**	–
Mobile × online discussion feature	–	.02*	–	.03	–	.04**
Total R² (%)	28.3***	29.7***	33.6***	36.8***	30.0***	29.6***

N = 1201. Entries are unstandardized regression coefficients.

***

p < .001; **p < .01; *p < .05.

Table 2.

An interactive effect of mobile information seeking on offline political participation at three values of discussion features.

	Effect	SE	t	p	LLCI	ULCI
Offline discussion frequency, −1 SD (−1.657)	−.045	.045	−0.986	.324	−.133	.044
Offline discussion frequency, mean (0.000)	.021	.034	0.609	.543	−.046	.088
Offline discussion frequency, +1 SD (1.657)	.086	.039	2.22	.026	.010	.163
Online discussion frequency, −1 SD (−1.926)	−.016	.043	−0.381	.703	−.101	.068
Online discussion frequency, mean (0.000)	.033	.034	0.981	.327	−.033	.100
Online discussion frequency, +1 SD (1.926)	.083	.042	1.994	.046	.001	.165
Offline network heterogeneity, −1 SD (−1.505)	−.080	.044	−1.80	.071	−.167	.007
Offline network heterogeneity, mean (0.000)	.014	.033	0.438	.661	−.050	.079
Offline network heterogeneity, +1 SD (1.505)	.109	.036	2.98	.003	.037	.180
Online network heterogeneity, −1 SD (−1.737)	−.048	.043	−1.10	.268	−.132	.037
Online network heterogeneity, mean (0.000)	.027	.033	0.83	.405	−.037	.091
Online network heterogeneity, +1 SD (1.737)	.102	.038	2.69	.007	.028	.176

SD: standard deviation; SE: standard error; LLCI: lower limit confidence interval; ULCI: upper limit confidence interval.

Figure 1.

Interactive effects of mobile information seeking and offline discussion frequency on offline political participation.

The second column in Table 1 shows that mobile information seeking significantly interacted with online discussion frequency in predicting offline political participation (B = .02, p < .05). As shown in Table 2, the association between mobile information seeking and offline political participation was significant and positive when frequency of online political discussion was high (effect = .083, CIs: [.001, .165]) but was not significant when it was low (effect = −.016, CIs: [−.101, .068]) and moderate (effect = .033, CIs: [−.033, .100]). Figure 2 visualizes this interaction pattern.

Figure 2.

Interactive effects of mobile information seeking and online discussion frequency on offline political participation.

H2 expected that mobile information seeking would have a stronger association with offline political participation for those who discuss politics with a larger number of others (a) face to face and (b) online. The data did not provide support for this hypothesis. The third and fourth columns in Table 1 indicate that mobile information seeking did not significantly interact with offline and online discussion network size in predicting offline political participation.

H3 proposed that mobile information seeking would have a stronger association with offline political participation for those who discuss politics with a greater diversity of others (a) face to face and (b) online. This hypothesis was supported. The fifth column in Table 1 shows that mobile information seeking significantly interacted with offline network heterogeneity in predicting offline political participation (B = .06, p < .01). A further analysis of this interaction in Table 2 indicates that the association between mobile information seeking and offline political participation was significant and positive when the level of offline network heterogeneity was high (effect = .109, CIs: [.037, .180]) but was not significant when it was low (effect = −.080, CIs: [−.167, .007]) and moderate (effect = .014, CIs: [−.050, .079]). This interaction pattern is visualized in Figure 3.

Figure 3.

Interactive effects of mobile information seeking and offline network heterogeneity on offline political participation.

Next, the sixth column in Table 1 shows that mobile information seeking significantly interacted with online network heterogeneity in predicting offline political participation (B = .04, p < .01). As shown in Table 2, the link between mobile information seeking and offline political participation was significant and positive when the level of online network heterogeneity was high (effect = .102, CIs: [.028, .176]) but was not significant when it was low (effect = −.048, CIs: [−.132, .037]) and moderate (effect = .027, CIs: [−.037, .091]). The interaction pattern is visualized in Figure 4.

Figure 4.

Interactive effects of mobile information seeking and online network heterogenity on offline political participation.

H4 predicted that mobile information seeking would have a stronger association with online political participation for those who discuss politics with others (a) face to face and (b) online more frequently. This hypothesis was supported. The first and second columns in Table 3 indicate that mobile information seeking significantly interacted with both offline and online discussion frequency (B = .05, p < .001 and B = .06, p < .001, respectively). As shown in Table 4, the association between mobile information seeking and online political participation was not significant when frequency of face-to-face political discussion was low (effect = .049, CIs: [−.011, .109]) but was significant and positive as frequency of face-to-face political discussion was moderate (effect = .123, CIs: [.077, .169]) and high (effect = .197, CIs: [.145, .249]). This interaction pattern is visualized in Figure 5.

Table 3.

An interactive effect of mobile information seeking and offline and online discussion features on online political participation.

	Online political participation
	Discussion frequency		Network size		Network heterogeneity
Age	−.01***	−.01***	−.02***	−.02***	−.01***	−.01***
Gender	−.07	−.07	−.09	−.08	−.01	−.03
Education	.03	.02	.01	.01	.03	.02
Income	−.01	−.01	.01	.01	−.00	−.01
Race	−.04	−.04	−.00	−.01	−.01	−.01
Political ideology	.03	.02	.07*	.06*	.04	.04
Political interest	.13***	.13***	.26***	.27***	.15***	.15***
Offline discussion feature	.01	−.01	−.06	−.07	.07	.04
Online discussion feature	.60***	.59***	.25***	.25***	.48***	.49***
Traditional news use	.09**	.08*	.22***	.21***	.15***	.14***
Online news use	−.12***	−.10***	−.18***	−.17***	−.15***	−.14***
Mobile information seeking	.12***	.13***	.35***	.35***	.20***	.21***
Mobile × offline discussion feature	.05***	–	.02	–	.08***	–
Mobile × online discussion feature	–	.06***	–	.03	–	.08***
R² total (%)	67.2***	67.7***	49.0***	49.1***	61.5***	61.9***

N = 1201. Cell entries are unstandardized regression coefficients.

***

p < .001; **p < .01; *p < .05.

Table 4.

An interactive effect of mobile information seeking on online political participation at three values of discussion features.

	Effect	SE	t	p	LLCI	ULCI
Offline discussion frequency, −1 SD (−1.657)	.049	.031	1.59	.112	−.011	.109
Offline discussion frequency, mean (0.000)	.123	.023	5.28	.000	.077	.169
Offline discussion frequency, +1 SD (1.657)	.197	.026	7.47	.000	.145	.249
Online discussion frequency, −1 SD (−1.926)	.029	.029	1.00	.319	−.028	.085
Online discussion frequency, mean (0.000)	.135	.023	5.91	.000	.090	.180
Online discussion frequency, +1 SD (1.926)	.241	.028	8.62	.000	.186	.296
Offline network heterogeneity, −1 SD (−1.505)	.090	.033	2.74	.006	.026	.155
Offline network heterogeneity, mean (0.000)	.205	.024	8.40	.000	.157	.253
Offline network heterogeneity, +1 SD (1.505)	.319	.027	11.81	.000	.266	.372
Online network heterogeneity, −1 SD (−1.737)	.080	.032	2.52	.012	.018	.142
Online network heterogeneity, mean (0.000)	.212	.024	8.87	.000	.165	.259
Online network heterogeneity, +1 SD (1.737)	.345	.028	12.40	.000	.290	.399

SD: standard deviation; SE: standard error; LLCI: lower limit confidence interval; ULCI: upper limit confidence interval.

Figure 5.

Interactive effects of mobile information seeking and offline discussion frequency on online political participation.

Similarly, the relationship between mobile information seeking and online political participation was not significant when frequency of online political discussion was low (effect = .029, CIs: [−.028, .085]) but was significant and positive when frequency of online political discussion was moderate (effect = .135, CIs: [.090, .180]) and high (effect = .241, CIs: [.186, .296]). This interaction pattern is visualized in Figure 6.

Figure 6.

Interactive effects of mobile information seeking and online discussion frequency on online political participation.

H5 expected that mobile information seeking would have a stronger association with online political participation for those who discuss politics with a larger number of others (a) face to face and (b) online. The third and fourth columns in Table 4 indicate that mobile information seeking did not significantly interact with offline and online discussion network size in predicting online political participation. Thus, this hypothesis was not supported.

H6 predicted that mobile information seeking would have a stronger association with online political participation for those who discuss politics with a greater diversity of others (a) face to face and (b) online. The data supported this hypothesis. The last two columns in Table 3 show that mobile information seeking had a significant interactive association with both offline and online discussion network heterogeneity (B = .08, p < .001, for both offline and online forms of network heterogeneity). To unpack the interaction patterns, Table 4 shows that the magnitude of the link between mobile information seeking and online political participation was stronger, as the level of network heterogeneity offline increased from 1 SD below the mean (effect = .090, CIs: [.026, .155]) to the mean (effect = .205, CIs: [.157, .253]) to 1 SD above the mean (effect = .319, CIs: [.266, .372]). Similarly, the strength of the linkage between mobile information seeking and online political participation was enhanced, as the level of online network heterogeneity increased. These interaction patterns are visualized in Figures 7 and 8.

Figure 7.

Interactive effects of mobile information seeking and offline network heterogeneity on online political participation.

Figure 8.

Interactive effects of mobile information seeking and online network heterogeneity on online political participation.

Discussion

The results reported above offer useful insights and extend the literature in several respects. First, the association between mobile-based information seeking and offline political participation was greater for those who talked about politics with other people more frequently face to face. This moderating role of face-to-face political discussion frequency was extended to online political discussion frequency and further to predict online political participation. The results are consistent with prior studies (Hardy and Scheufele, 2005; Nisbet and Scheufele, 2004; Scheufele, 2002). By frequently discussing politics with others, people can learn and think more about, and relate more closely to, political issues and events than simply receiving political news and information via mobile phones, which leads to deeper engagement with politics.

The unique characteristics of mobile phones described earlier seem to play a role in facilitating political discussion. For example, when one hangs out with friends at home, cafés, restaurants, and other places, or while walking from one place to another, she/he may use a mobile phone to visit a news site to find out election results, based on which she/he may exchange political opinions with others. The capacity of mobile phones to enable such seamless, on-the-go styles of communication activities is not comparable with more traditional devices such as a desktop and laptop computer. As mobile media are becoming a preferred way to obtain online news for many citizens (Matsa and Lu, 2016), they may offer a unique opportunity for political engagement. For example, news sites might integrate a mobile-optimized notification system showing comments recently posted to an article people have read to encourage them to read those comments and also post their own thoughts. Using mobile devices’ location information, news sites and search engines might show ongoing or upcoming nearby political events and ask users to share what they have in mind about related issues.

Second, the relationship between mobile information seeking and offline and online political participation was greater for those who discussed politics with people of different backgrounds both face to face and online. The literature on political discussion points to the value of discussing politics with people of different sociocultural and political backgrounds (Eveland and Hively, 2009; Kwak et al., 2005; Scheufele et al., 2004, 2006; Valenzuela et al., 2012). When one talks with such individuals, the chances are high that she/he encounters varied political views and even disagreements. Such exposure to differences pushes one to think carefully about one’s own ideas, dissect opposing views, and build counterarguments (Scheufele et al., 2006). It also pushes one to carefully consume news and information to prepare for future discussions that will likely involve differences and disagreements (Scheufele et al., 2006). Discussion with people of different backgrounds is not limited to interpersonal settings. People of different genders, races, ages, religious beliefs, and political ideologies and affiliations, among others, use online media to actively exchange political views. Doing so seems to produce more meaningful political results than simply seeking political information with mobile phones.

Third, in contrast to discussion frequency and network heterogeneity, the size of a discussion network, both offline and online, did not moderate the association between mobile information seeking and offline and online forms of political participation. Thus, although research has shown the political benefit of having a large discussion network (e.g. Eveland and Hievely, 2009; Gil de Zúñiga et al., 2012; Kwak et al., 2005), in terms of the differential gains model (Scheufele, 2002), it may not be the size of a discussion network that makes mobile information seeking an effective source of political participation. Given the role of discussion frequency and heterogeneity found in our study, actual discussion may be what makes informational use of mobile phones politically meaningful. That is, from a differential gains perspective, a large discussion network may not be politically important until it is activated in actual discussion (McLeod et al., 1999).

Finally, as shown in Table 2, the association between mobile information seeking and offline political participation was significant only when the value of discussion frequency and heterogeneity, both offline and online, was high. In contrast, mobile information seeking had a significant association with online political participation even at moderate levels of offline and online discussion frequency, and even if the level of offline and online discussion heterogeneity, respectively, was low (see Table 4). These findings appear to point to different levels of effort needed to engage in politics offline and online. Although the literature shows that personal resources, such as time, income, and civic skills, and interest in politics are major theoretical explanations for offline political participation (Verba et al., 1995), the relative ease and convenience of online political acts, such as making campaign donations online, might allow citizens, particularly traditionally inactive ones, to be participatory (Gibson and Cantijoch, 2013; Hamilton and Tolbert, 2012). In this respect, online participation is sometimes regarded as slacktivism or activities that have little tangible political impact yet make participants feel good about themselves (Morozov, 2009). However, offline and online participation are not necessarily mutually exclusive. Certain activities, such as making campaign donations and signing a petition, overlap across offline and online platforms. This overlap suggests that performing an online political activity is a relatively low-cost entry point into politics, leads to performance of the corresponding activity offline, and progresses further into other traditional offline activities such as voting and participating in rallies and protests.

Despite these unique insights, this study faces a few limitations that must be explicitly acknowledged. First, the data were based on an online panel of participants who volunteered to participate in the survey, instead of a probability-based sample. The sample obtained in this manner does not represent the population, which necessarily limits the generalizability of the findings. To address this issue, future research should use traditional probability sampling techniques or use an online panel, such as GfK KnoweldgePanel, selected based on probability sampling techniques. Second, as with previous research on the differential gains model, this study did not test its theoretical assumptions. The hypothesized role of discussion in enhancing the link between mobile information seeking and political participation was based on differential learning and anticipation of discussion. Future work should employ an experimental design to assess which of the mechanisms plays a larger role in fostering political participation.

Third, drawing from the differential gains model, we specified political discussion as moderating the relationship between mobile information seeking and political participation. Yet, alternative models might be equally plausible. For example, politically active individuals might seek political information more with mobile phones, as they talk about politics with other people more frequently, with a larger number of other people, and with a greater diversity of other individuals. Similarly, political interest could be positioned as an antecedent factor that orients people to seek political information using mobile phones (McLeod et al., 2002), which leads to political participation, and this indirect relationship is moderated by different forms of political discussion. Testing these potential alternative models with causally robust data would help better understand the interplay among mobile use, political discussion, and political engagement.

Finally, the measurement scheme of mobile information seeking and online political discussion and participation can be refined by tapping specific information seeking, discussion, and participation activities uniquely enabled by mobile phones in order to examine whether the observed findings are mobile specific or attributed broadly to Internet use across digital devices (e.g. desktops, laptops, tablets, and mobile phones). For example, the ease and convenience of mobile phones might make citizens engage in political acts online more quickly than when they use other digital devices, which might explain why the link between mobile information seeking and online political participation was significant even at moderate levels of discussion frequency and heterogeneity, as noted above. To more clearly assess how mobile-unique activities promote political participation, more refined measures of the key theoretical variables are necessary.

These limitations notwithstanding, this study has extended the literature on the political utility of mobile media. Our findings suggest that it is frequency and heterogeneity of discussion that enhance the influence of mobile information seeking on political participation. We encourage future research to elaborate on the current findings. In addition to addressing the above limitations, future work could examine motivations for mobile information seeking, which could be integrated into a moderated mediation model where motivations for mobile information seeking are specified as affecting political participation through mobile information seeking behavior, and this indirect relationship is specified to be moderated by different types of political discussion. Given the constantly evolving nature of mobile media, continued research is needed to better understand how mobile media uniquely, or jointly with other communication activities, facilitate citizen engagement in politics.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Masahiro Yamamoto (PhD, Washington State University, 2012) is an assistant professor in the Department of Communication at the University at Albany, State University of New York. His research interests include communication in community contexts, civic and political participation, and social media. His work has appeared in journals such as Communication Research, Journalism & Mass Communication Quarterly, Mass Communication & Society, and New Media & Society.

Seungahn Nah (PhD, University of Wisconsin-Madison, 2006) is an associate dean for Graduate Affairs and Research in the School of Journalism and Communication at the University of Oregon. His research centers on the interrelationships among communication, community, and democracy with special emphasis on the roles of digital communication technologies in community and democratic processes and outcomes. His work has appeared in journals such as Journal of Communication, International Journal of Communication, Journal of Computer-Mediated Communication, New Media & Society, Mass Communication & Society, Journalism & Mass Communication Quarterly, Journal of Broadcasting & Electronic Media, and Journalism: Theory, Practice and Criticism, among others.

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