Mobile donation in America

Abstract

Mobile donation has gained mainstream media attention since the Haiti Earthquake Relief in 2010. Yet, there is still a lack of research on mobile donation as a new venue of technology-mediated civic engagement. Using nationally representative survey data, this research examines the variations in mobile donation by mobile phone usage patterns and user characteristics such as age, race, and socioeconomic status. Results show that the diversity of mobile phone use and the frequency of relational mobile communication are positively associated with mobile donation. As importantly, mobile donation helps to overcome age, race and SES gaps, giving members of disadvantaged social groups a more accessible tool for civic engagement. Internet donation and mobile donation complement rather than compete with each other.

Keywords

civic engagement digital divides donation mobile communication U&G

As an integral part of modern communication, the mobile phone has implications for civic engagement during major social or political events (Rheingold, 2002) and in daily life (Campbell & Kwak, 2010a, 2010b, 2011a, 2011b; Ling, 2004). Donating money to social or political causes is an important form of civic engagement (Putnam, 2000). A growing number of non-profit organizations have used text-to-give campaigns for fundraising. However, there has been a lack of scholarly research on mobile donation. This omission is critical as mobile donation offers a unique opportunity to examine a new practice of technology-mediated civic engagement.

Using nationally representative survey data collected by the Pew Internet and American Life Project, this research aims to fill this gap by focusing on three questions. First, how does mobile donation vary by the diversity of mobile phone use and the frequency of general and specific mobile communication practices? Second, does the mobile phone, as a more widely adopted technology than the Internet, give members of disadvantaged groups a more accessible tool for civic engagement? Third, does mobile donation supplement or replace Internet donation? Answering these questions has both theoretical and practical implications.

A growing number of people use mobile phones to access the Internet. Accordingly, mobile donation may include donation via text message and donation via mobile Internet. In this research, the term mobile donation is defined as donation via text message. This definition is widely used by non-profit organizations, mobile carriers, the mass media, and an emerging body of literature on this topic.

Background

Two-thirds of adult Americans have made a charitable donation (Smith, Schlozman, Verba, & Brady, 2009). American individuals and households donated $212 billion in 2010, accounting for almost three-quarters of all monetary donations in the country (Giving USA Foundation, 2011). The Internet has provided an innovative venue for donation and has been widely used by political campaigns, non-profit organizations, and individuals (Vargas, 2008). The proportion of online donors among adult American Internet users had tripled from 7% in 2001 to 22% in 2010 (Pew, 2011).

Compared to Internet donation, mobile donation is relatively new in the U.S. Inspired by the text-to-donate efforts employed by the London Live 8 Concert, American entrepreneur James Eberhard founded Mobile Accord/mGive in 2005, which has become the major intermediary for mobile donation campaigns in the U.S. (Mobile Accord, 2012). Text-to-donate is the main method of mobile donation. Mobile phone users can donate to a charity a predetermined amount (normally $5 or $10 per text message) by texting a keyword to a short code. The donated amount would be added to the donor’s monthly phone bill by the mobile carrier.

The Haiti Earthquake Relief in 2010 helped mobile donation gain mainstream media attention. By texting the keyword “HAITI” to the short code 90999, a user could donate $10 to American Red Cross’ text-to-give campaign, which raised $22 million in the first week and eventually contributed 8% of the total donations American Red Cross raised for the Haiti Earthquake Relief (Heath, 2010). It is estimated that four million Americans donated a total of more than $40 million via text message to the Haiti Earthquake Relief organized by American Red Cross and other nonprofit organizations (Smith, 2012). During the Texas wildfire relief in September 2011, the United Way of Capital Area in Texas implemented a text-to-donate option. Although mobile donation made up less than 4% of the total monetary donation, mobile donors made up 70% of all donors (personal communication, 2011). Both examples illustrate that mobile donation can rapidly mobilize a large number of small donors. Although the amount of mobile donations can be modest, fundraisers see it as a “cultivation step” for transforming spontaneous donors into regular supporters (mGive Foundation, 2011, p.4). Indeed, 56% of Haiti text donors contributed to more recent disaster relief efforts such as the BP oil spill or the Japanese tsunami (Smith, 2012).

Given the rapid mobilization and the cultivation potential, mobile giving is expected to grow in terms of the number of donors and the amount of donations. Text message donation to political campaigns was finally approved by the Federal Election Commission in June 2012, thanks to multi-year bi-partisan lobbying efforts. The ruling was considered a “game-changer” for political campaigns (Gillum, 2012). In addition, both the Obama and the Romney campaigns used mobile apps to raise money in the 2012 General Election Cycle (Bilton, 2012).

Mobile phone and civic engagement

As a typical case of research being left behind by practices in the field of digital communication, current knowledge about mobile donation is mainly from journalistic or marketing research, while scholarly research examining factors contributing to individuals’ adoption of mobile donation is emerging (Smith, 2012; Weberling & Waters, 2012). Furthermore, the analysis from the practitioners has centered on technological affordance. “Easy and convenient” has been celebrated as the major appeal of text-to-give campaigns by journalists and activists (American Red Cross, 2010). However, users’ characteristics and usage patterns can be as important to mobile donation as technology affordances. In particularly, there has been a lack of research on how the embeddedness of the mobile phone in users’ everyday life and its popularity among young people and racial minorities would help fundraisers to reach previously un- or under-tapped pools of donors.

We position mobile donation in the emerging literature on the civic implications of mobile communication. “Smart mobs” in the Philippines used text messages to organize protests, which led to the downfall of President Joseph Estrada (Rheingold, 2002). Citizens in Myanmar and Iran used mobile phones to document and expose the brutality of authoritarian regimes. The Arab Spring showed how mobile technologies served as a powerful tool for political mobilization. The London riot in 2011 offered a reminder that mobile phones could be used for less constructive purposes too. Despite concerns that people may use mobile technologies to privatize public space and withdraw from casual face-to-face encountering (Smith, 2011a), studies show that certain aspects of mobile phone use facilitate civic engagement (Campbell & Kwak, 2010a, 2010b, 2011a, 2011b; Donner, 2008; Ling, 2004, 2008). The literature is, however, inconclusive on the links between general and specific types of mobile communication and civic engagement and almost non-existent on mobile donation.

Integrating the Uses & Gratifications (U&G) approach (Katz, Blumler, & Gurevitch, 1973; Ruggiero, 2000) and the digital divide literature (Selwyn, 2004; Chen & Wellman, 2005), we draw on the growing body of literature on civic implications of mobile communication and the larger literature on the social implications of the Internet to develop hypotheses and research questions on how usage patterns, user characteristics, and Internet donation are related to mobile donation.

Usage patterns

The U&G approach offers an audience-centered explanation of media usage, especially on how users actively choose media platforms and content to gratify their cognitive, affective, or social needs (Ruggiero, 2000). Combining communicative, networking, and media feature in one single device, the mobile phone allows users to conduct a variety of instrumental and expressive activities: seeking information, especially news and public affairs (informational use), relaxation and entertainment (recreational use), maintaining social relationships with family, friends, and colleagues (relational use, also termed as hyper-coordination), and readjusting meeting time and place when needed (logistical use, also termed as micro-coordination, Ling, 2004; see also Campbell & Russo, 2003; Katz, 2008). We examine two aspects of usage patterns and their relationships with mobile donation: the overall diversity of mobile phone use and the frequency of mobile communication.

The diversity of mobile phone use

Drawing on the U&G approach, Leung and Wei (2000) found that college students used mobile phones to gratify a variety of needs: sociability, immediacy, mobility, and security. Users are physically and psychologically attached to their mobile phones as a symbol of “me, my mobile and my identity” (Vicent, 2006, p.42). Close to two-thirds of smartphone users slept with their smartphones and more than a third of iPhone users frequently used it at the dinner table (TeleNav, 2011). According to the U&G approach, the more a medium is used habitually, the greater the user’s exposure and affinity to that medium (Rubin, 1984). As the tool of choice when users conduct many aspects of their daily life, the mobile phone has become a hub and a symbol of a digital lifestyle (Horrigan, 2009). People who are more comfortable with mobile technologies are more engaged in civic and political activities (Campbell & Kwak, 2010a, 2010b). Compared to general mobile phone users, mobile donors to the Haiti Earthquake Relief were more likely to use mobile phones to exchange emails, take photos, and record videos (Smith, 2012). Accordingly, the more integrated the mobile phone in the mundane ways a user socializes, communicates, works, relaxes, or simply gets less bored, the higher the likelihood it will be used for civic engagement. Thus,

H1: Diverse mobile phone use is positively related to the likelihood of mobile donation.

The frequency of mobile communication

The mobile phone accommodates pervasive, portable, and perpetual communication from the most public to the most private places (Katz & Aakhus, 2002). One study on the American general population finds that both the number of voice calls and the number of text messages exchanged on the previous day are positively associated with social leisure activities but not with voluntary group participation (Campbell & Kwak, 2010a, p. 442). By contrast, a study on EU teens shows that text messages but not voice calls are positively related to voluntary membership (Ling, Yttri, Anderson, & Diduca, 2003). One study of college students in several Asian cities demonstrates on the one hand that the number of daily text messages and voice calls is positively related to social leisure activities and on the other hand that the number of daily voice calls is positively related to group involvement (Chen et al., 2012).

These mixed results motivate Campbell and Kwak (2010b) to call for fine-tuned analysis on the specific types of mobile communication practices. Frequent informational use across media platforms contributes to civic and political participation (Norris & Ingelhart, 2009; for mobile informational use see Campbell & Kwak, 2010b, 2011b; Rojas & Puig-i-Abril, 2009). Recreational mobile phone use is positively associated with civic engagement (Campbell & Kwak, 2010b).

Due to data limitations, our analysis in this article focuses on the two core types of mobile communication: relational and logistic mobile communication (Ling, 2004). Relational mobile communication is also termed as hyper-coordination and refers to using the mobile phone to keep contacts with family and friends. A study on the general American population shows no significant relationships between relational mobile communication and aggregated indices of civic engagement and political participation (Campbell & Kwak, 2010b, p.541). Yet, a study of college students in Asian cities reveals a positive association between relational mobile communication and several specific indicators of civic engagement: social leisure activities, group involvement, and interpersonal trust (Chen et al., 2012). Mobile phones make people’s networks increasingly mobile and mobilizable: more than 40% of the text donors to the Haiti Earthquake Relief encouraged friends or family members to make a similar donation (Smith, 2012). Yet, it is not clear how hyper-coordination would affect users’ network size and composition, which may, in turn, affect civic engagement (Gil de Zúñiga & Valenzuela, 2011).

Logistical mobile communication is also termed as micro-coordination, which refers to “nuanced instrumental coordination” that allows users to check out the accessibility and location of their family members, friends or colleagues and readjust meeting time and place when needed (Ling & Yttri, 2002, p.139). Hatching and changing plans on the go makes daily life more instant, spontaneous, and flexible. As micro-coordination often involves people meeting up in public places, it increases the serendipity of meeting and talking with new people co-presented on the spot (Campbell & Kwak, 2011a; Hampton, Livio, & Goulet, 2010). Micro-coordination may also be involved in the phenomenon of flash mobs when a large number of anonymous people seemingly spontaneously pop up in a public place, “briefly performed some collective synchronous action, and then dissolved back into ‘the general public’” (Kluitenberg, 2006, p.7). Flash mobs can be performative, political, or both (Walker, 2011). Practitioners speculate that text-to-give campaigns make donation more spontaneous and give donors instant satisfaction of contributing to a shared cause, especially at large events such as concerts and political or religious gatherings (Heath, 2010). As the literature remains inconclusive on the links between mobile communication and civic engagement in general and almost non-existent on mobile donation, we propose a research question:

RQ1: How are the frequency of general mobile communication and the frequency of specific mobile communication related to the likelihood of mobile donation?

User characteristics

Both technology use and civic engagement can vary by age, socioeconomic status (SES), and race (Norris, 2001; Putnam, 2000). Older, better educated, more affluent, and white Americans are more likely to donate than younger, less educated, less affluent, and non-white Americans (Andreoni, Brown, & Rischall, 2003; Bekkers, 2010; Lee & Chang, 2007). The age, race, and SES gaps in offline civic engagement often become smaller or sometimes even muted in online civic engagement as the latter tends to be less demanding of participants’ time and resources (Shah, Kwak, & Holbert, 2001; Jensen, Danziger, & Venkatesh, 2007). Yet, compared to offline donors, online donors are younger but remain more affluent (Rubanenko, 2006). In what follows, we discuss how user characteristics may affect mobile donation.

Younger people are more likely to make political use of social network sites (SNSs) or post political content online than older people (Smith et al., 2009). They also lead older people in the breadth and depth of mobile phone use (Horrigan, 2009). For instance, more than 90% of Millenials (Americans aged 18–34) use mobile phones and they exchange an average of 110 text messages daily, almost three times the national average (Smith, 2011b). Journalistic and marketing research has highlighted the great potential of mobile donation in reaching young people. Millenials made up more than 40% of the mobile donors of the American Red Cross Haiti text campaign (Woyke, 2010) and one-third of the mobile donors in a survey of more than 200 non-profit organizations (mGive Foundation, 2011).

Better education and more privileged class background are related to more Internet use for political participation (Zillien & Hargittai, 2009). Yet, the SES gaps in civic engagement narrow through blogging or SNSs (Smith et al., 2009). Low cost makes text messaging a popular mobile phone feature among people with low SES. The amount of daily text messages exchanged is the highest among less educated and low income users (Smith, 2011b). The modest amount of mobile donations may enable low income users to donate by text message. Yet, marketing research shows that college or postgraduate educated Americans are more likely than high school graduates to engage in mobile donations (mGive Foundation, 2011).

African Americans used to be less likely to access the Internet and to spend less time on the web than white Americans (Lenhart et al., 2003). However, African Americans and Hispanics are more likely to use mobile phones and exchange more text messages than white Americans (Smith, 2011a, 2011b). There seems to be a racial gap in Internet donation: one-quarter of white Internet users, 17% of African American Internet users, and 14% of Hispanic Internet users donated online in 2010 (Pew, 2011). However, compared to traditional channel donors, mobile donors to the Haiti Earthquake Relief were more racially diverse (Smith, 2012). Thus,

H2: Younger, better educated, more affluent, and minority mobile phone users are more likely to engage in mobile donation than older, less educated, less affluent, and white mobile phone users.

Internet donation and mobile donation

If mobile donation provided a more convenient venue of donation and narrowed socio-demographic gaps in civic engagement, would it replace or supplement conventional ways of donation, especially Internet donation? Studies show that giving to one type of charity increased the likelihood of giving to other types of charity (Reinstein, 2011). Yet, there has been limited research on the relationships between traditional and digital modes of donation. One study suggests that online donations help non-profit organizations to reach donors who have little or no previous giving history (Rubanenko, 2006). In addition, most mobile donors have used other channels of giving, among which Internet donation is the most preferred channel (mGive Foundation, 2011). Thus,

H3: Users who have used online donation are more likely to engage in mobile donation.

Method

This research draws on a national random sample survey conducted by the Pew Internet and American Life Project from April 29 to May 30 in 2010. The survey used a combination of landline and cellular random digit dial (RDD) samples to represent adults in continental America who have access to a landline or mobile telephone. The response rate was 22% for the landline and 19% for the mobile sample. To assure generalizability, data were weighted based on demographic parameters derived from the Census Bureau’s March 2009 Annual Social and Economic Supplement (for details of the survey method and weighting see Smith, 2010). The sample included a total of 2252 adult Americans, among which 1756 were Internet users and 1917 were mobile phone users. A total of 779 mobile phone users were asked the question whether they ever made a charitable donation by text message and among them 777 respondents gave a valid answer.¹ Thus, our analysis started with the 777 respondents.

As shown in Table 1, column 3, key independent variables contained missing values. For instance, the frequency of daily text messages was missing in 91 observations.² Missing value analysis suggested no systematic pattern of missing data (Little & Rubin, 2002). Compared with other techniques of handling missing data such as the listwise deletion or the single imputation, multiple imputation is stimulation based and provides less biased parameter estimates (Rubin, 1987). We thus ran Stata’s mi command to conduct multiple imputation and used 20 imputations as recommended (StataCorp, 2011). The analysis sample based on the imputed data had 753 respondents (Table 1).³

Table 1.

Descriptive statistics.

	Unweighted Observed Data						Weighted Observed Data		Imputed Data^a
	N	Missing	Mean	S.D.	Min	Max	Mean	S.D.	Mean	S.E.
Mobile donation	777	0	0.10	0.31	0	1	0.11	0.32	0.10	0.01
Internet donation	741	36	0.28	0.45	0	1	0.28	0.45	0.26	0.02
Gender (Female=1)	777	0	0.50	0.50	0	1	0.47	0.50	0.48	0.02
Age	766	11	40.98	16.06	18	95	36.16	13.51	37.05	0.55
White Americans	766	11	0.66	0.47	0	1	0.61	0.49	0.61	0.02
African Americans	766	11	0.16	0.37	0	1	0.16	0.37	0.16	0.02
Hispanics	766	11	0.10	0.30	0	1	0.14	0.35	0.15	0.02
Other racial groups	766	11	0.08	0.26	0	1	0.08	0.28	0.08	0.01
Education	775	2	4.80	1.59	1	6	4.64	1.58	4.53	0.07
Income	687	90	5.60	2.33	1	9	5.35	2.31	5.29	0.10
Diversity of mobile use index	775	2	4.56	1.75	1	7	4.92	1.64	4.78	0.07
Daily phone calls	749	28	17.41	34.59	0	500	20.46	39.18	20.00	1.66
Daily text messages	686	91	43.73	94.80	0	500	48.68	97.03	51.02	4.62
Relational mobile communication	704	73	9.67	4.60	0	20	10.21	4.61	9.99	0.20
Logistical mobile communication	706	71	8.23	4.13	0	16	8.75	4.13	8.50	0.18

based on weighted observed data, N=753.

Measures

Mobile donation, the dependent variable, was dichotomous, coded as 1 if the respondent had used text message to make a charitable donation and 0 if not. Internet donation was also dichotomous, coded as 1 if the respondent had used the Internet to make a charitable donation and 0 if not. In the analysis sample (N=753), about 10% of the respondents had donated via texting and 26% had donated online.

The diversity of mobile phone use was the sum score of seven dichotomous items (M= 4.78, S.E.=0.07), including whether respondents had used the mobile phone to 1) send or receive email, 2) take a picture, 3) play music, 4) send or receive instant messages, 5) record a video, 6) play a game, and 7) access the Internet.

The frequency of general mobile communication was measured by the number of daily voice calls and the number of daily text messages, respectively. Respondents were asked the number of mobile phone voice calls they made and received on a typical day with answers ranging from 0 to 500 or more (M= 20, S.E.= 1.66). Using the same scale, respondents were asked the number of text messages they sent or received on a typical day (M= 51.02, S.E.=4.56).

The frequency of specific mobile communication.

Using a 0–4 point scale (0=never to 4=several times a day), respondents were asked how often they used mobile phones for relational and logistical mobile communication. Relational mobile communication included five items: 1) just say hello and chat, using voice call and text message, respectively, 2) have a long conversation to discuss important personal matters, using voice call and text message, respectively, and 3) sent or received text messages quietly when in a setting where they could not make a voice call (M= 9.99, S.E.=0.20). The Cronbach’s α of the five items was 0.72. Logistical mobile communication included four items of using voice call and text message, respectively: 1) report where they are or check on where someone else is, and 2) coordinate where they are physically meeting someone (M= 8.50, S.E.= 0.18) . The Cronbach’s α of the four items was 0.75.

User characteristics included age, race, education, and income. Respondents’ average age was 37.05. Race had 4 categories: White Americans (61%), African Americans (16%), Hispanics (15%), and other racial groups (8%). Education was measured by a 1–6 point scale (1= less than high School to 6=postgraduate education, M=4.53, S.E.=0.07). Family income was measured by a 1–9 point scale (1=less than $10000 to 9=$150,000 or more, M=5.29, S.E.=0.10). Gender was controlled and coded as 1 if the respondent was female and 0 male. Women made up 48% of the analysis sample.

Results

As the dependent variable was dichotomous, we used the imputed data to fit three logistic regression models and examined the relationships of user characteristics, usage patterns, and Internet donation with the likelihood of mobile donation (Table 2).^4,5 Model 1 only contained socio-demographic variables including gender, age, race, and SES. None of them had significant impacts on the likelihood of mobile donation. Model 2 further took into account usage patterns. The diversity of mobile phone use was positively related to the likelihood of mobile donation (b=0.25, p<=0.05). The frequency of relational mobile communication was also positively related to mobile donation (b=0.12, p<=0.01). Yet, the frequency of general mobile communication and the frequency of logistical mobile communication were not significantly associated with mobile donation. Model 3 examined the relationship between Internet and mobile donation and showed that Internet donation was positively related to mobile giving (b=1.97, p<=0.001).

Table 2.

Logistic regression of mobile donation.

	Model 1			Model 2			Model 3
	Coef	S.E	p	Coef	S.E	p	Coef	S.E	p
Gender (Female=1)	−0.09	0.28		−0.09	0.28		0.07	0.30
Age	−0.01	0.01		0.02	0.01		0.01	0.01
Race (White Americans as reference)
African Americans	0.52	0.34		0.26	0.38		0.55	0.40
Hispanics	0.08	0.50		−0.21	0.45		0.39	0.48
Other racial groups	0.31	0.48		0.23	0.51		0.53	0.50
Education	0.16	0.10		0.20	0.10		0.08	0.11
Income	0.05	0.06		0.01	0.07		−0.07	0.07
Diversity of mobile use index				0.25	0.10	*	0.18	0.11
Daily phone calls				0.00	0.00		0.00	0.00
Daily text messages				0.00	0.00		0.00	0.00
Relational mobile communication				0.12	0.05	**	0.13	0.05	*
Logistical mobile communication				−0.02	0.04		−0.03	0.05
Internet donation							1.97	0.35	***
_cons	−2.85	0.52	***	−6.55	1.00	***	−5.89	1.03	***
Model F test⁵	0.88			2.85		***	5.44		***
N	755			753			753

***

p<0.001, **p<0.01, *p<0.05

To summarize, H1, which predicted that diverse mobile phone use would be related to the likelihood of mobile donation, was supported. RQ1 asked how general and specific mobile communication patterns were associated with mobile donation. The frequency of relational mobile communication was positively related with the likelihood of mobile donation. However, the number of daily voice calls, the number of daily text messages, and the frequency of logistical mobile communication had no significant relationship with mobile donation. H2 was rejected as user characteristics such as age, race, income and education did not significantly affect the likelihood of mobile donation. H3 was supported as there was a positive relationship between Internet and mobile donation.

In order to compare whether user characteristics affected Internet and mobile donation differently, we fitted one logistical regression model on the likelihood of Internet donation (Table 3). For the analysis of Internet donation, we included all Internet users and yield an analysis sample of 1719 respondents after using multiple imputation to account missing values. The result demonstrated significant age and SES gaps in Internet donation. Younger Internet users were more likely to donate online than older Internet users (b=-0.01, p <=0.01). Better educated Internet users were more likely than less educated Internet users to donate online (b=0.38, p<=0.001). Family income significantly increased the likelihood of online donation (b=0.19, p<=0.001). However, there was no significant gender or racial gap in the likelihood of online donation.

Table 3.

Logistic regression of internet donation.

	Coef.	Std. Err.	p
Gender (Female=1)	0.06	0.14
Age	−0.01	0.00	**
Race (White Americans as reference)
African Americans	−0.29	0.25
Hispanics	−0.44	0.29
Other racial groups	−0.18	0.29
Education	0.38	0.06	***
Income	0.19	0.04	***
_cons	−3.57	0.36	***
Model F test	15.70		***
N	1719

***

p<0.001, **p<0.01, *p<0.05

Discussion and conclusion

Mobile donation is an emerging form of technology-mediated civic engagement. However, current knowledge about mobile donation has been by and large limited to journalistic or marketing research highlighting technology affordances. Integrating the U&G approach’s attention to usage patterns and the digital divide framework’s focus on how social inequalities shape individual’s access to, use of, and gain from technologies, we position mobile donation in the emerging literature on the civic implications of mobile communication technologies.

Using nationally representative survey data collected by the Pew Internet and American Life Project, this research examines how mobile donation varies by usage patterns and user characteristics. We are interested in whether the mobile phone, as a more widely adopted technology than the Internet, gives members of disadvantaged social groups a more affordable tool for civic engagement. As each wave of new communication technologies has triggered a debate about whether they would replace existing modes of communication, this research is also interested in the relationship between Internet and mobile donation. We have several findings.

First, mobile donation is still in its early stages as only 10% of respondents have donated via text message. Second, mobile donation helps to overcome age, racial, and SES barriers in traditional donation venues, especially when compared with the persistent age, education, and income gaps in online donation. Compared to computers and the Internet, the low cost and low skill barriers allow mobile phones to diffuse faster and reach broader socioeconomic strata. Our findings echo the existing literature that the mobile phone offers a more practical way to bridge digital divides and thus facilitates the social inclusion of disadvantaged social groups more so than the PC-based Internet (Burrell, 2010; Chen & Wellman, 2005; Donner, 2008). On the one hand, our research offers empirical support to the notion that mobile donation gives non-profit organizations “an opportunity to connect with a new generation of supporters” (American Red Cross, 2010). On the other hand, our analysis demonstrates that this new generation of supporters is not necessarily young. Although marketing research has stressed the appeal of mobile donation to young people, our analysis shows that the mobile phone helps to turn both young and old users from diverse racial and class background into donors. This research thus lends support to the mobilization thesis, which argues that new technologies facilitate a more informed and more engaged public by reducing information, communication, and coordination cost (Norris, 2001). Third, Internet donation increases rather than decreases the likelihood of mobile donation, which resonates with the normalization hypothesis that new technologies tend to empower and mobilize those who are already active (Agre, 2002).

Fourth, the portfolio of mobile donors partially fits Rogers’ categorization of early adopters of an innovation as being socially and technically well-connected (1962). Diverse mobile phone use and frequent relational mobile communication are associated with a greater likelihood of mobile donation. By contrast, the amount of daily voice calls and text messages is not related to mobile giving, which resonates with previous studies that the frequency of general mobile communication is not associated with voluntary group involvement (Campbell & Kwak, 2010a). Although the existing literature suggests that micro-coordination may contribute to spontaneous social interaction in the public space (Campbell & Kwak, 2011a; Hampton et al., 2010), logistical mobile communication is not related to mobile donation in this research. Overall, our analysis suggests that it is not about how many calls or text messages a user exchanges on a daily basis; it is the embeddedness of mobile phones in various domains of the user’s daily life: communicating, displaying identity, generating and consuming media content as well as managing their interpersonal networks. A greater diversity of mobile phone use indicates a greater immersion in a mobile-phone mediated lifestyle, which increases the likelihood of mobile donation.

It is tempting to assume that it is up to the users to pick up the most convenient or socially appropriate technology to engage in social action. Although mobile donation narrows the digital divides by attracting donors from more diverse age, racial and SES groups, there are still many layers of computational and social barriers that hinder a wider adoption of mobile donation. While non-profit organizations often claim that text-to-donate is instant, convenient, and critical when immediate relief responses are needed (American Red Cross, 2010), the reality is more complicated. Most non-profit organizations have to work through a Mobile Application Service Provider (MASP) such as mGive, Wireless Foundation, or MobileCause as the intermediary between them and the mobile carriers. The MASPs charge non-profit organizations for using their mobile campaign platforms and for each successfully completed donation transaction (Mobile Accord, 2012). In addition, the mobile carriers may charge transaction fees and hold the donated money for 60 to 90 days.

The research has several limitations. First, the survey did not ask the respondent to which specific cause or charity the donation was made. Yet, the survey was conducted about four months after the Haiti Earthquake. Intensive media coverage and the relative novelty of mobile donation employed for the relief efforts may attract more people to donate by text message. For instance, almost nine out of ten Haiti mobile donors saw the Text-to-Haiti campaign on TV and 50% of them donated immediately (Smith, 2012). Thus, our results could be affected by the salience of the Haiti Earthquake relief efforts. Future research may also need to draw on the literature of crisis communication and examine how traditional media and digital media increase situational awareness as well as the willingness to help victims of natural disasters (Seo et al., 2012; Starbird & Palen, 2011; Vieweg, Hughes, Starbird, & Palen, 2010; Weberling, 2010). Second, the survey provided no data on the informational mobile phone use. Yet, previous studies have identified a consistent relationship between informational use and civic engagement across traditional, digital and mobile media platforms (Campbell & Kwak, 2011a, 2011b). Third, due to data limitations, we were not able to examine whether users’ network size, structure and composition may mediate or moderate the relationship between usage patterns and mobile donation (Campbell & Kwak, 2011b; Gil de Zúñiga & Valenzuela, 2011). Thus, future research should take into account how traditional and digital media exposure, informational mobile phone use, and users’ network characteristics may affect mobile donation during both disaster relief and ordinary times. As importantly, future research needs to take into consideration a variety of psychological factors that motivate people to give such as self-efficacy or trust (Cheung & Chan, 2000).

Despite these limitations, this research offers one of the first systematic analyses of mobile donation in the general population and makes several contributions. By revealing the significance of diverse mobile phone use and frequent relational mobile communication, the research shows that the U&G approach is useful in examining emerging technology-mediated practices. In particular, our research shows that the impacts of technology and media use depend on how and for what purposes they are used. By demonstrating how mobile donation narrows digital divides and offers a more accessible venue for civic engagement, it supports the mobilization thesis. By identifying a positive relationship between Internet donation and mobile donation, it shows that traditional and mobile modes of civic engagement complement rather than compete with each other.

Given the growing embeddedness of mobile phones in users’ daily life and the popularity of relational mobile communication across the world, mobile donation has great potential for quickly reaching a large number of previously under-tapped small donors from a wider range of social strata: young and old, whites and minorities, high and low SES. The social and technical practices of mobile donation are rapidly evolving and a growing number of social and political causes are using it as a venue for raising funds and awareness. We hope this article will inspire more research on mobile donation and other emerging civic engagement enabled by mobile and other new communication technologies.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

Author biographies

Wenhong Chen is an assistant professor in the Department of Radio-TV-Film, College of Communication, at the University of Texas at Austin. Her research interests include the implications of new communication technologies in entrepreneurial, organizational and multi-ethnic settings. Her research has been published in Journal of Computer-Mediated Communication, Information, Communication & Society, The Information Society, Entrepreneurship Theory & Practice, City & Community, and Management and Organization Review.

Travis Givens earned his MA in Media Studies from the Department of Radio-TV-Film, College of Communication, at the University of Texas at Austin, in 2011. He is currently an advertising strategist with a digital acquisition firm based in Austin, TX. His primary interests include mobile mediated interaction with advertising on the internet and through apps.

References

Agre

P. E.

(2002). Real-time politics: The Internet and the political process. The Information Society, 18(5), 311–331. doi: 10.1080/01972240290075174

American Red Cross. (2010). FAQ mobile giving. Retrieved from: https://american.redcross.org/site/SPageServer?pagename=%2Fntld_Haiti_text2help_faqs#Q1

Andreoni

Brown

Rischall

(2003). Charitable giving by married couples. Journal of Human Resources, XXXVIII(1), 111–133. doi: 10.3368/jhr.XXXVIII.1.111

Bekkers

(2010). Who gives what and when? A scenario study of intentions to give time and money. Social Science Research, 39, 369–381. doi: https://dx-doi-org.web.bisu.edu.cn/10.1016/j.ssresearch.2009.08.008

Bilton

(2012, January 30). Obama and Romney campaigns adopt Square for funding. The New York Times. Retrieved from http://bits.blogs.nytimes.com/2012/01/30/obama-and-romney-campaigns-adopt-square-for-funding/

Burrell

(2010). Evaluating shared access: Social equality and the circulation of mobile phones in rural Uganda. Journal of Computer-Mediated Communication, 15(2), 230–250. doi: 10.1111/j.1083–6101.2010.01518.x

Campbell

S.W.

Kwak

(2010a). Mobile communication and social capital: An analysis of geographically differentiated usage patterns. New Media & Society, 12(3), 435–451. doi: 10.1177/1461444809343307

Campbell

S. W.

Kwak

(2010b). Mobile communication and civic life: Linking patterns of use to civic and political engagement. Journal of Communication, 60(3), 536–555. doi: 10.1111/j.1460–2466.2010.01496.x

Campbell

S. W.

Kwak

(2011a). Mobile communication and civil society: Linking patterns and places of use to engagement with others in public. Human Communication Research, 37(2), 207–222. doi: 10.1111/j.1468–2958.2010.01399.x

10.

Campbell

S. W.

Kwak

(2011b). Political involvement in “mobilized” society: The interactive relationships among mobile communication, network characteristics, and political participation. Journal of Communication, 61(6), 1005–1024. doi: 10.1111/j.1460–2466.2011.01601.x

11.

Campbell

S. W.

Russo

T. C.

(2003). The social construction of mobile telephony: An application of the social influence model to perceptions and uses of mobile phones within personal communication networks. Communication Monographs, 70(4), 317–334. doi: 10.1080/0363775032000179124

12.

Chen

V.-H.

Wei

Zhang

(2012). A comparative study of the relationship between mobile phone use and social capital among Asian college students. Paper presented at the Mobile Communication, Community and Locative Media Practices: From the Everyday to the Revolutionary Preconference, International Communication Association, Phoenix, AZ.

13.

Chen

Wellman

(2005). Minding the gaps: The digital divide and social inequality. In Romero

Margolis

(Eds.), Blackwell Companion to Social Inequalities (pp. 523–545). Oxford, UK: Blackwell.

14.

Cheung

C.-K.

Chan

C.-M.

(2000). Social-cognitive factors of donating money to charity, with special attention to an international relief organization. Evaluation and Program Planning. 23, 241–253. doi: https://dx-doi-org.web.bisu.edu.cn/10.1016/S0149–7189(00)00003–3

15.

Donner

(2008). Research approaches to mobile use in the developing world: A review of the literature. The Information Society, 24(3), 140–159.

16.

Gil

Zúñiga

Valenzuela

(2011). The mediating path to a stronger citizenship: Online and offline networks, weak ties, and civic engagement. Communication Research, 38(3), 397–421. doi: 10.1177/0093650210384984

17.

Gillum

(2012, June 11). FEC allows campaign donations via text messaging. The Miami Herald. Retrieved from http://www.miamiherald.com/2012/06/11/2844665/fec-allows-campaign-donations.html

18.

Giving USA Foundation. (2011). Annual report on philanthropy.

19.

Hampton

K. N.

Livio

Goulet

L. S.

(2010). The social life of wireless urban spaces: Internet use, social networks, and the public realm. Journal of Communication, 60(4), 701–722. doi: 10.1111/j.1460–2466.2010.01510.x

20.

Heath

(2010, January 19) U.S. cellphone users donate $22 million to Haiti Earthquake Relief via text. The Washington Post. http://www.washingtonpost.com/wp-dyn/content/article/2010/01/18/AR2010011803792.html

21.

Horrigan

(2009). The mobile difference. Washington, DC: Pew Internet and American Life Project.

22.

Jensen

M. J.

Danziger

J. N.

Venkatesh

(2007). Civil society and cyber society: The role of the Internet in community associations and democratic politics. Information Society, 23(1), 39–50. doi: 10.1080/01972240601057528

23.

Katz

Blumler

J. G.

Gurevitch

(1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509–523. doi: 10.1086/268109

24.

Katz

J.E.

Aakhus

(2002). Conclusion: Making meanings of mobile – a theory of apparatgeist. In Katz

J.E.

Aakhus

(Eds.), Perpetual contact: Mobile communication, private talk, public performance (pp. 301–318). Cambridge, UK: Cambridge University Press.

25.

Katz

J.E.

(2008). Handbook of mobile communication studies. Cambridge, MA: MIT Press.

26.

Kluitenberg

(2006). The network of waves. Open, 11, 6–17.

27.

Lee

Chang

(2007). Who gives what to charity? Characteristics affecting donation behavior. Social Behavior and Personality, 35(9), 1173–1180.

28.

Lenhart

Horrigan

Rainie

Allen

Boyce

Madden

O’Grady

. (2003). The ever-shifting Internet population: A new look at Internet access and the digital divide. Washington, DC: The Pew Internet and American Life Project.

29.

Leung

Wei

(2000). More than just talk on the move: Uses and gratifications of the cellular phone. Journalism & Mass Communication Quarterly, 77(2), 308–320. doi: 10.1177/107769900007700206

30.

Ling

Yttri

(2002). Hyper-coordination via mobile phones in Norway. In Katz

J.E.

Aakhus

(Eds.) Perpetual contact: Mobile communication, private talk, public performance (pp. 139–169). Cambridge, UK: Cambridge University Press.

31.

Ling

(2004). The mobile connection: The cell phone’s impact on society. San Francisco, CA: Morgan Kaufman.

32.

Ling

(2008). New tech, new ties: How mobile communication is reshaping social cohesion. Cambridge, MA: MIT Press.

33.

Ling

Yttri

Anderson

DiDuca

(2003). Mobile communication and social capital in Europe. In Nyiri

(Ed.), Mobile democracy: Essays on society, self, and politics (pp. 359–373). Vienna: Passagen Verlag.

34.

Little

R. J. A.

Rubin

D. B.

(2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: Wiley-InterScience.

35.

mGive Foundation (2011). Text giving donor survey report. http://www.mgive.com/Studies/DonorSurveyReport.aspx.

36.

Mobile Accord. (2012). The Mobile Accord and mGive story. http://www.mgive.com/AboutUs.aspx

37.

Norris

(2001). Digital divide: Civic engagement, information poverty, and the Internet worldwide. Cambridge, UK: Cambridge University Press.

38.

Norris

Inglehart

(2009). Cosmopolitan communications: Cultural diversity in a globalized world. New York: Cambridge University Press.

39.

Pew Internet and American Life Project. (2011). Trend Data (Adults). http://www.pewinternet.org/Data-Tools/Download-Data/Trend-Data.aspx

40.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J.-Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. doi: 10.1037/0021–9010.88.5.879

41.

Putnam

R.D.

(2000). Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.

42.

Reinstein

(2011). Does one charitable contribution come at the expense of another? http://www.bepress.com/bejeap/vol11/iss1/art40

43.

Rheingold

(2002). Smart mobs: The next social revolution. New York: Basic Books.

44.

Rogers

E. M.

(1962). Diffusion of innovations. New York: Free Press.

45.

Rojas

Puig-i-Abril

(2009). Mobilizers mobilized: Information, expression, mobilization and participation in the Digital Age. Journal of Computer-Mediated Communication, 14(4), 902–927. doi: 10.1111/j.1083–6101.2009.01475.x

46.

Rubanenko

(2006). Online gift donor profile. Charleston, SC: Blackbaud, Inc.

47.

Rubin

A.M.

(1984). Ritualized and instrumental television viewing. Journal of Communication, 34(3), 67–77. doi: 10.1111/j.1460–2466.1984.tb02174.x

48.

Rubin

D. B.

(1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

49.

Ruggiero

T. E.

(2000). Uses and gratifications theory in the 21st century. Mass Communication and Society, 3(1), 3–37. doi: 10.1207/s15327825mcs0301_02

50.

Selwyn

(2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341–362. doi: 10.1177/1461444804042519

51.

Seo

Sun

Merolla

A. J.

Zhang

(2012). Willingness to help following the Sichuan Earthquake: Modeling the effects of media involvement, stress, trust, and relational resources. Communication Research, 39, 3–25. doi: 10.1177/0093650210388407

52.

Shah

Kwak

Holbert

(2001). “Connecting” and “disconnecting” with civic life: Patterns of Internet use and the production of social capital. Political Communication, 18(2), 141–162. doi: 10. 1080/105846001750322952

53.

Smith

(2010). Mobile access 2010. Washington, DC: Pew Internet & American Life Project.

54.

Smith

(2011a). Americans and their cell phones. Washington, DC: Pew Internet & American Life Project.

55.

Smith

(2011b). Americans and text messaging. Washington, DC: Pew Internet & American Life Project.

56.

Smith

(2012). Real time charitable giving. Washington, DC: Pew Internet & American Life Project.

57.

Smith

Schlozman

K. L.

Verba

Brady

(2009). The Internet and civic engagement. Washington, DC: Pew Internet & American Life Project.

58.

Starbird

Palen

(2011). “Voluntweeters”: Self-organizing by digital volunteers in times of crisis. Proceedings of the 2011 annual conference on Human factors in computing systems, CHI ’11, (pp. 1071–1080). New York, NY: ACM.

59.

StataCorp. (2011). Stata multiple imputation reference manual release 12. Stata Press. College Station, TX.

60.

TeleNav Inc. (2011). Survey finds one-third of Americans more willing to give up sex than their mobile phones. http://www.telenav.com/about/pr-summer-travel/report-20110803.html.

61.

Vargas

J. A.

(2008). Obama raised half a billion online. The Washington Post. http://voices.washingtonpost.com/44/2008/11/20/obama_raised_half_a_billion_on.html.

62.

Vieweg

Hughes

A. L.

Starbird

Palen

(2010). Microblogging during two natural hazards events: What twitter may contribute to situational awareness. Proceedings of the 28th international conference on human factors in computing systems, CHI ’10, (pp. 1079–1088). New York, NY: ACM.

63.

Vincent

(2006). Emotional attachment and mobile phones. Knowledge, Technology & Policy, 19(1), 39–44. doi: 10.1007/s12130–006–1013–7

64.

Walker

(2011). Eight is not enough: A historical, cultural, and philosophical analysis of the flash mob. Unpublished doctoral dissertation, Louisiana State University. http://etd.lsu.edu/docs/available/etd-04112011–102646/

65.

Weberling

(2010). Mobilizing disaster relief: U.S. media coverage and public response to the tsunami and Pakistan earthquake. International Communication Research Journal, 45(3–4), 50–80.

66.

Weberling

Waters

(2012). Gauging the public’s preparedness for mobile public relations: The ‘Text for Haiti’ campaign. Public Relations Review, 38(1), 51–55.

67.

Woyke

(2010). What the Red Cross learned from its Haiti mobile campaign. Forbes. http://www.forbes.com/sites/elizabethwoyke/2010/08/26/what-the-red-cross-learned-from-its-haiti-mobile-campaign/

68.

Zillien

Hargittai

(2009). Digital distinction: Status-specific types of Internet usage. Social Science Quarterly, 90(2), 274–291.