The Effect of Individuals’ Organization Affiliation on Their Internet Donations

Abstract

Based on a national representative sample of U.S. internet users, this article examines the impact of associational participation on the likelihood of making an online donation to a charity. The results indicated that internet users engaged in more offline groups and networks are more likely to donate online. Frequency of use of internet and social media do not influence general propensity to donate, thereby suggesting that online donations are a function of actual engagement in social groups, rather than of frequent exposure to the internet media. Individuals involved in choice-based groups were the most likely to donate online, compared to other types of organization participation and/or affiliation. The authors also find that general propensity to donate online (including charities respondents are unaffiliated with) and making monetary contributions specifically to the particular organizations individuals are active in have somewhat distinct determinants.

Keywords

donations charities internet survey fundraising public opinion

Introduction

Although the potential for the internet to have an impact on nonprofit organizations is substantial, it is not maximized. The intricacies of volunteer and donor recruitment, social media marketing, and fundraising are being actively explored to inform new models of citizen engagement to achieve greater outreach to clients and donors. Online fundraising has a special promise insofar that it allows mobilizing and recruiting donors, who may be unreachable through other methods. Therefore, it is essential to understand the broad determinants of online donation behavior to improve fundraising strategies – a topic of major interest to nonprofit organizations (Sargeant, West, & Jay, 2007; Saxton & Guo, 2011; Te’eni & Young, 2003; Waters, 2007).

Research examining the role of the internet in nonprofits is developing, though we still know little about the determinants of online donations (Hart, 2002; Ingenhoff & Koelling, 2009; Pitt, Keating, Bruwer, Murgolo-Poore, & de Bussy, 2001). It is unclear to what extent online donations are driven by online campaigns and marketing strategies versus other factors affecting individual donation behavior, beyond demographics and properties of the donation drives or campaigns. To advance a broader understanding of donation behavior, this article examines the role of citizens’ actual organization participation and/or affiliation (e.g., the number and type of groups and associations with which individuals affiliate) in online donation behavior. If an individual is already involved in multiple associations and groups, does that also make them more likely to pursue other avenues of involvement, such as charitable donations online? The question is motivated by a need to understand the drivers of donation behavior in terms other than demographic characteristics and internet usage patterns. Although this information can be somewhat useful, such variables do not provide many actionable prescriptions for donor planning, recruitment, and retention. Besides fundraising, the nonprofit sector’s sustainability is contingent upon citizen engagement.

This study examines the effect of participation in voluntary associations on the propensity to donate online. The underlying premise is that organization participation and/or affiliation in fact represents a form of civic engagement and, as such, is also one aspect of social capital (Schneider, 2007). Scholarly research to date is inconclusive regarding the impact of social capital on online behavior (Shah, Kwak, & Holbert, 2001; Wellman, Hass, Witte, & Hampton, 2001). Although social capital has been demonstrated as important for the growth and performance of the nonprofit sector and increasing leadership capacity (King, 2004; Saxton & Benson, 2005), it is unclear what is the place occupied by online donations in the nexus of civic engagement, involvement with causes and associations, and social capital. This study proposes that individuals’ involvement in various associations is an important element, and source, of individuals’ stocks of social capital. Accordingly, if social capital in general (and specifically one that results from organization participation and/or affiliation) is associated with greater civic involvement, then it may be plausible that a direct connection exists between specific aspects of social capital accumulation (e.g., association participation) and specific instances of tangible civic involvement (e.g., donations). If participation in networks and groups indeed increases the stock of individuals’ social capital, then one plausible consequence is that individuals will be more likely to contribute to the types of networks or groups that are the source of their social capital. However, an alternative conceptualization is also possible. Perhaps involvement with groups through online donations is a mechanism intended to compensate for the lack of social capital–generating mechanisms in respondents’ lives? Empirically addressing the connection between organization participation and/or affiliation and propensity to donate will shed some light on the likely mechanism.

Therefore, we tentatively propose a positive connection between organization participation and/or affiliation and online donations, one mediated by the process of accumulating social capital. For example, individuals who are more embedded in social relationships may possess higher level of social capital as a result of such involvement and, therefore, feel more compelled to support the connections that are the source of this capital as well as to seek new connections. Thus, it is plausible that the determinants of donation behavior are not merely discrete factors such as demographics, exposure, and so forth, but there may be a more complex relationship between online behaviors and sociodemographic characteristics (Nie, 2001) where the act of donating is simply a component of broader citizen involvement. Hence, this article examines whether higher levels of participation in voluntary associations (defined as memberships in civic groups and associations) results in a higher inclination to effect positive change, and one of the avenues to do so is donating to causes online.

This study examines this question through a national survey of internet users in the United States examining online donors and nondonors and participation in voluntary associations. The article is divided into the following sections. The literature review examines the academic literature on nonprofit organizations, discusses donor behavior and the internet, and examines social capital theory and the internet. This is followed by the Methods and Results discussing the statistical models used in the study. The Conclusion section summarizes the main findings of this article and discusses some of the practical implications of this research.

The Internet and Nonprofit Organizations

The internet has become an important fundraising tool. For example, a survey by the Chronicle of Philanthropy indicated that 6 in 10 charities were raising more money online in 2011 than they were in 2010 (Wallace, 2010). Charities are using the internet along with other vehicles to raise donations such as board giving, special events, foundation grants, and so forth. Of all of the different vehicles that have been used, online donations had the largest increase (Nonprofit Research Collaborative, 2011).

The internet also represents a unique opportunity for nonprofits to advance their missions through civic engagement (Suarez, 2009). Existing research suggests a need to understand the effects of the internet and other Information and Communication Technologies (ICT) have on nonprofit organizations (Te’eni & Young, 2003). For nonprofits to exploit the opportunities of the internet they need to know how it is integrated within people’s daily lives. Indeed, Hart (2002) has argued that the internet is not a replacement tool for traditional fundraising. The internet can be successfully used to build and enhance relationships with a prospective donor, which makes it more likely that the nonprofit will be able to solicit a gift from them. However, with the absence of the cultivation of such relationships, the availability of internet technology will not necessarily have a dramatic transformative effect.

Currently, most nonprofits mainly use the internet for providing information (Waters, 2007) as a one-way communication tool allowing for little two-way interaction. In an examination of Swiss fundraising nonprofit organizations, the internet similarly was used mostly for information needs (Ingenhoff & Koelling, 2009), not in its capacity to build relationships and create new dialog with stakeholders. Findings echoed by Goatman and Lewis (2007) of U.K. online fundraising capabilities found these were viewed as being more informational rather than relational. At present, nonprofit websites excel at providing information and opportunities to donate, but lag behind in their ability to stimulate citizen engagement (Sargeant, West, & Jay, 2007; Saxton & Guo, 2011; Tuckman, Chatterjee, & Muha, 2004).

In summary, the current research focuses more on accounting for the diffusion and use of internet and ICT among nonprofits, and the extent to which adoption of new media is passive or interactive (Goatman & Lewis, 2007; Saxton & Guo, 2011), whereas assessment of how, whether, and to what effect new technologies are integrated in organizational processes and interactions with stakeholders (e.g., donors) is still underdeveloped.

Understanding the Relationship Between Donor Behavior and ICT

The growing number of and competition among charities stimulates interest in learning more about the behavior of donors, especially as it applies to new and emerging technologies and their influence on donation behavior. Internet, email, and short messaging are generally considered important tools to enhance donor’s perception of the importance of the charity for giving (Pentecost & Andrews, 2010).

Existing literature discusses donor characteristics in relation to charitable giving (Bekkers, 2010; Croson, Handy, & Shang, 2010; Lee, Piliavin, & Call, 1999; Sargeant, 1999; Smith & McSweeney, 2007). A substantial portion of the literature on giving to charities focuses on giving in general, for example, in terms of time and/or money. A smaller group of studies (somewhat similar in focus to the present study) examine the different channels of giving, such as through the mail, phone, in person solicitations, and the internet (Pentecost & Andrews, 2010; Sargeant & Woodliffe, 2007), and generally suggest that charities need to target their message with the appropriate media (Pentecost & Andrews, 2010).

In a review of determinants of charitable behavior, Van Slyke and Brooks examined characteristics such as age, gender, marital status on charitable giving, as well as the religious, ideological, and educational factors. Income, wealth, and taxes also influence charitable giving. The digital divide literature shows that access to the internet by definition influences the likelihood of engaging in online transactions, including donations (Lee, Berniker, Wyhe, & Johnson, 2005). Besides social and demographic factors, donors may exhibit different patterns of donation behavior as a result of distinct motivations, such as altruism, sympathy, pride, and so forth (Pitt, Keating, Bruwer, Murgolo-Poore, & de Bussy, 2001), suggesting that understanding behavioral motivations can be useful for predicting future behavior and more effective fundraising strategies (Van Slyke & Brooks, 2005).

Nonprofit organizations also need to adapt to new donors who are younger, high-tech, and often wealthy individuals who may differ from the traditional donor (Wagner, 2002). In a comparison of the traditional philanthropist with the “new donor,” new donors were more entrepreneurial and socially conscientious, more likely to use the internet, or other technical means, to make donations or cultivate relationships; they may require different ways to reach them (Wagner, 2002).

The variety of determinants of donation behavior can be categorized by the extent they describe structural contextual constraints or individual cognitive experiences (Kottasaz, 2004; Sargeant, 1999). Extrinsic determinants are structural contextual factors such as age, gender, social class, income, and rural/urban setting. The intrinsic determinants describe the underlying individual motivations to support a charity. Some studies suggest that extrinsic factors such as age, income, and education play a stronger role in determining giving behavior (Lee & Chang, 2007; Sargeant, 1999; Smith & McSweeney, 2007), whereas other research shows that donor behavior is more focused on intrinsic factors such as socialization with a feeling of a moral obligation (Lee, Piliavin, & Call, 1999).

Our study proposes a somewhat different approach insofar that participation in voluntary associations is neither a purely internal, subjective experience, nor purely a structurally determined one. For example, whereas opportunities for joining groups and organization participation and/or affiliation in general may be partially dependent on context, individual behaviors and values can affect the extent to which an individual is actually involved in groups (e.g., by actively seeking them out locally, or online). Accordingly, the extent of involvement with different groups is in itself likely to affect individual internal states and values. As a result, donation behavior is no longer usefully conceptualized as a static variable affected by “extrinsic” or “intrinsic” factors, but rather as a correlate of the interplay between structural and subjective factors, as mediated by organization participation and/or affiliation. Since social capital is one of the likely dimensions of such interplay, below we propose an interpretation of the effect of organization participation and/or affiliation on online donations as possibly meditated by social capital.

Social Capital and the Internet

In an examination of the differences between social capital and civic engagement Schneider (2007) states that civic engagement presumes the whole community could benefit from an activity, whereas social capital does not necessarily seek to help anyone beyond the network. Nevertheless, such distinction is hard to justify in the abstract, without considering relevant contextual factors. One could envision scenarios, in which social capital and community concerns overlap, with social capital as a mediating factor between involvement in the community and broader concerns, perhaps leading to an overlap between the two—an approach followed in this study. Support for such reasoning exists in research showing that individuals who engage in communities of participation such as schools, soup kitchens, and weekend soccer leagues are more likely to engage in charitable giving (Schervish & Havens, 1997), as well as social capital in general may foster charitable giving (Wang & Graddy, 2008). Group participation may be completely voluntary (e.g., volunteering at a soup kitchen), whereas others may be mediated by circumstances (e.g., extracurricular sports programs for parents of school-aged children). Since participation fosters social capital and identification with group causes, such participation may also foster commitment to other causes, as argued here, opening the possibility that social capital influences civic engagement (Schneider, 2009). People can develop social capital through participation in voluntary associations, and this participation serves as the building block for greater civic engagement and healthier communities (Schneider, 2009), by fostering personal interaction in a community (Kenworthy, 1997).

Involvement with nonprofits, groups, and associations represents a dimension of social capital in action insofar that such participation is either motivated by a certain interest or concern (e.g., in choice-based groups) or facilitates relationship building and advancement of common concerns (e.g., in circumstance-based groups). The pathways between participation in groups and social capital may be twofold—individuals may build relationships to voluntarily enter into associations because of an interest to address collective needs, or the enrichment of their relationships could result from their participation in certain groups (King, 2004). Community participation could conceivably not only add to but also exemplify social capital through increased civic participation (Mesch & Talmud, 2010). This is indirectly supported by studies on the nonprofit sector detecting a relationship between the accumulation of social capital and the growth in the nonprofit sector (Saxton & Benson, 2005).

In an analysis of whether the internet increases, decreases, or supplements social capital, Wellman and colleagues (2001) found that the most likely impact was to supplement existing social capital that is, facilitate existing relationships. This contradicts the idea that the internet is a discrete force directly stimulating positive change in peoples’ lives by creating new forms of online interaction and overcoming constraints to establish relationships. In this view, the internet is an extension of offline activities, essentially supplementing these activities and not replacing them. Internet use also appears to affect broader forms of participation, for example, in terms of political participation, especially in reaching younger groups that have traditionally been inactive offline (Gibson, Lusoli, & Ward, 2005; Livingston, Bober, & Helsper, 2005). Other scholars (e.g., Ellison, Steinfield, & Lampe, 2007) have found that university students using social media (e.g., Facebook) were more likely to engage in the formation and maintenance of connections (i.e., social capital).

An alternative view suggests that the internet decreases social capital because it diverts people away from their immediate physical environment and alienates them from one another. Nie (2001) argues that because of the inelasticity of time, internet users may actually reduce social capital—time spent on internet activities is time not spent interacting with people offline. A problem with this view is the implicit assumption that internet usage necessarily must come at the expense of social interaction, rather than any of the multiple other possible alternative uses of time. Indeed, other studies show that internet use and social capital development depend on the content of internet use (Shah, Kwak, & Holbert, 2001). Although behaviors related to seeking information online are positively correlated with social capital, using the internet primarily for recreation, such as gaming, appears to be negatively related to social capital.

Overall, the research on social capital in relation to online behaviors is inconclusive, and no precedent exists focusing on a link between social capital, as seen through the involvement of individuals in voluntary associations, and the propensity to donate to charities online. Building on the insights from previous studies, this article proposes that online donation behavior is likely a “spillover” of organization participation and/or affiliation, and in doing so, tentatively adopts the “complementarity hypothesis” (e.g., Mesch & Talmud, 2010; Wellman et al., 2001) that proposes that internet usage and social capital are complements rather than substitutes in a sense that we expect online charitable giving behavior to be essentially a consequence of social capital–accumulating activities, whether occurring online, offline, or in a combination of the two. At a time when more and more conventional activities are conducted online, identifying the distinction between online and offline behaviors will be perhaps less useful than understanding the content and purpose of these behaviors. Hence, the focus of this study is on the relationship between participation in voluntary associations, one important element of social capital, examined through the likelihood to donate to charitable organizations over the internet.

Hypothesis

This study attempts to isolate specifically a relationship between an individuals’ level of participation in voluntary associations and the likelihood of donating online. Hence,

Hypothesis 1: Participation in voluntary associations increases the likelihood of donating to a charity online.

This hypothesis is justified based on the general expectation that higher level of organization participation and/or affiliation may either signify, or lead to, greater levels of social capital. Certainly, the internet has provided opportunities for participation in various causes that are not necessarily present locally (e.g., in respondents’ own communities, neighborhood, and related local associations). The internet has made possible modes of association and organization that transcend the association activities made possible and fostered by local connections. Involvement in local and wide-scale associations, causes, and communities are not necessarily driven by the same social and relationship logics and concerns. Local organization participation and/or affiliation can be driven both by choice (e.g., local reading groups) and circumstance (e.g., by simply living in a certain neighborhood, and being part of the homeowners’ association, children clubs, and so forth—group activity driven by living circumstances). Although both types of groups (choice- and circumstance-based) represent organization participation and/or affiliation, which are one important aspect of social capital, it is possible that they have distinct impacts on the propensity to donate, and our empirical models attempt to distinguish such effects as outlined in the analysis section.

Data and Variables

This study is based on a telephone survey on Americans’ use of the internet conducted by the Pew Research Center in November 2010. Princeton Survey Research Associates International interviewed a nationally representative sample of 2,303 adults. However, the data analyzed below only include respondents who are internet users (N = 1,810) because the dependent variable has no relevance for people who do not use the internet.

The main dependent variable for this study is coded 1 if an individual has used the internet to make a donation, 0 otherwise, representing the individual response to the question, “Do you ever use the internet to make a donation to charity?” The variable is generic, reflecting any donation to any charity (not just donations to charitable organizations in which respondents may be directly involved in other ways).

A second dependent variable used for comparison is a binary dependent variable coded 1 if a respondent has made a monetary contribution (through any means, online or not) to an organization they are directly involved with (e.g., through membership or some other form of direct participation), 0 otherwise. This second variable is utilized in two of the models presented below to assess whether general propensity to donate to charity, and donations specifically to organizations in which they are directly involved in, have distinct determinants.

The independent variables for the study include several composite measures of associational activities, based on responses to multiple survey items asking respondents about their membership in 26 different group types, associations, and so forth. Descriptive statistics for the individual items used to construct the index are presented in Table 1. Although relational data do not contain indications of the “value” of all connections, the sum of connections is arguably a proxy for the general “inclinations’ to participate. Furthermore, we attempt to decompose different “inclinations” to participate as outlined below.

Table 1.

Individual Survey Items for the Associational Participation Index.

Please Tell Me If You Are Currently Active in Any of These Types of Groups or Organizations, or Not	M (%)	Max
Community groups or neighborhood associations	24.4	1
Church groups or other religious or spiritual organizations	46.4	1
Sports or recreation leagues, whether for yourself or for your child	28.5	1
Hobby groups or clubs	21.9	1
Performance or arts groups, such as a choir, dance group, or craft guild	13.6	1
Professional or trade associations for people in your occupation	27.4	1
Parent groups or organizations, such as the PTA or local parent support group	15.2	1
Youth groups, such as the Scouts, YMCA, or 4-H	11.8	1
Social or fraternal clubs, sororities, or fraternities	10.8	1
Veterans groups or organizations such as the American Legion or VFW	7.6	1
Literary, discussion or study groups, such as a book club or reading group	13.7	1
Charitable or volunteer organizations, such as Habitat for Humanity or the Humane Society	28.8	1
Consumer groups, such as AAA (Triple A) or coupon sharing groups	29.9	1
Farm organizations	4.8	1
Travel clubs	7.3	1
Sports fantasy leagues	7.8	1
Gaming communities	5.4	1
National or local organizations for older adults, such as AARP	19.9	1
Political parties or organizations	19.9	1
Ethnic or cultural groups	6.0	1
Labor unions	9.4	1
Support groups for people with a particular illness or personal situation	19.8	1
Alumni associations	21.2	1
Fan groups for a particular TV show, movie, celebrity, or musical performer	6.1	1
Fan groups for a particular sports team or athlete	10.7	1
Fan groups for a particular brand, company, or product	3.8	1
Environmental groups	10.0	1

Based on the individual responses, a summative index of organization participation and/or affiliation was constructed with values ranging from 0 to 26. The index exhibits an acceptable reliability (Cronbach’s alpha = .78). Based on the Additive Index, a second operationalization was developed. Since one-unit change in the Associational Participation Index is difficult to interpret substantively, we also created a variable classifying respondents in qualitatively distinct groups based on the quartile they occupy in the distribution of the composite associational participation variable. The Summative Index was used to create an ordinal variable consisting of four categories defined by the quartile of the distribution of the associational participation variable in which respondent’s fall —if a respondent’s value of associational participation falls in the first quartile of the distribution, in the new variable they will be coded as “1” and so forth. Table 2 provides the summary statistics of the associational participation measures and control variables.

Table 2.

Descriptive Statistics of Variables.

Variable	Observations	M (%)	SD	Min	Max
Do you ever use the internet to make a donation to a charity online?	1,810	31.2	0.46	0	1
Contributed money to a group you are active in?	1,810	63	0.48	0	1
Associational Participation Index	1,810	4.37	3.71	0	26
Associational Participation Index: 1st quartile	1,810	26.9	0.45	0	1
Associational Participation Index: 2nd quartile	1,810	31.2	0.46	0	1
Associational Participation Index: 3rd quartile	1,810	22.5	0.42	0	1
Associational Participation Index: 4th quartile	1,810	19.4	0.40	0	1
“Choice-based group participation” (Factor 1)	1,810	2.12	2.15	0	11
“Circumstance-based group participation” (Factor 2)	1,810	0.4	0.83	0	3
“Consumer interests—fan club-based group participation” (Factor 3)	1,810	0.28	0.63	0	4
Age	1,810	47.58	18.12	18	99
Do you ever use the internet to use a social networking site like MySpace, Facebook, or LinkedIn.com?	1,810	57.7	0.49	0	1
Male	1,810	46.6	0.50	0	1
White	1,810	79.2	0.41	0	1
Family income: below US$20,000	1,502	14.1	0.40	0	1
Family income: US$20,000-US$50,000	1,502	31.0	0.46	0	1
Family income: US$50,000-US$100,000	1,502	33.3	0.47	0	1
Family income: US$100,000 or more	1,502	21.6	0.41	0	1
Has a college degree or higher	1,803	41.9	0.49	0	1
Lives in a rural area	1,788	21.8	0.41	0	1
Has children	1,804	29.3	0.46	0	1
Married or cohabiting with a partner	1,810	61.1	0.49	0	1
Uses the internet daily	1,804	66.1	0.47	0	1
Intensity of involvement in groups scale	1,513	2.4	1.4	0	4
Took a leadership role in a group you are active in	1,810	35	0.47	0	1
Attended meeting or events for a group you are active in	1,810	71	0.45	0	1
Volunteered your time to a group you are active in	1,810	66	0.48	0	1

In addition to the key dependent and independent variables noted above, the model also includes a set of controls to rule out alternative explanations for donation behaviors previously identified in the donation behavior literature (Smith & McSweeney, 2007). Existing research shows that there normally is a relationship between sociodemographic factors and giving and volunteering for charities (Bekkers, 2010; Croson, Handy, & Shang, 2010; Kottasz, 2004). Research shows that the likelihood of donating is positively related to: income, education level, marital status, and living in a rural area (although the latter could also have a negative effect according to the digital-divide literature if it is associated with unequal access to technology—geographically, or by demographic groups (Livingstone, Bober, & Helsper, 2005; Mesch & Talmud, 2010; Shah, Kwak, & Holbert, 2001). From the digital-divide rationale, age, marital status, and education also could have a negative effect on online giving insofar as they may present constraints to accessing the internet in some circumstances (Smith & McSweeny, 2007).

Accordingly, the proposed models include respondent’s age in years, while also controlling for gender, race, income, education, and living environment, all of which may be correlated with opportunities, propensities, and ability to make charitable donations. In addition, we also control for marital status (important both socioeconomically and as an alternative proxy for social connectedness) and whether or not the respondent has children. Finally, we control for extent of internet usage to represent the relative sophistication of the user with the internet and social media, using binary variables coded as 1 if a respondent is a daily internet user, or social media user, and 0 otherwise, respectively.

Finally, considering the variety of possible associations and organizational and community connection types possible, we perform an exploratory (principal factor) factor analysis in an attempt to identify different dimensions/factors describing different latent types of inclinations for association involvement. Although a generic composite measure of the overall organization participation and/or affiliation of an individual is helpful and theoretically meaningful, organization participation and/or affiliation can happen for many reasons and circumstances and may have an underlying structure that helps to at least partially categorize different patterns of association. Principal factor analysis seeks to extract the maximum number of factors capable of accounting for the common variance and, thus, more appropriate for identifying conceptually meaningful groupings. Since the potential factors conceptually are likely to be mutually correlated, oblique rotation was used. The factor analysis generates 3 factors that cumulatively account for 89% of the items’ covariation; thus, we limit the discussion to these 3 factors.¹

A factor loading cutoff value of 0.32 was used to isolate variables represented by a particular factor (Tabachnick & Fidell, 2001). The resulting clusters of variables appear meaningful (though the reliability of some is ambiguous). Considering the exploratory nature of this decomposition of organization participation and/or affiliation, the resulting groupings are used in summative indices to approximate potentially conceptually meaningful dimensions of affiliating with different types of groups and associations (the results and effects of these indices need to be interpreted cautiously until efforts to explicitly validate them are made in the future.) Table 3 provides descriptive information regarding the three factors identified (loadings, and Cronbach’s alpha coefficients).

Table 3.

Factor Analysis.

Survey Items	Factor 1 (“Choice-Based Group Participation”) Loadings, Cronbach’s Alpha = .7	Factor 2 (“Circumstance-Based Group Participation”) loadings, Cronbach’s Alpha = .55	Factor 3 (“Consumer Interests—Fan Club–Based Group Participation”) loadings, Cronbach’s Alpha = .50
Community groups or neighborhood associations	0.38
Performance or arts groups, such as choir, dance groups	0.41
Social or fraternal clubs, sororities, or fraternities	0.4
Literary, discussion, or study groups, such as book or reading group	0.36
Charitable or volunteer organizations, such as Habitat for Humanity or the Humane Society	0.34
Consumer groups, such as AAA or coupon sharing groups	0.39
National or local organizations for older adults, such as AARP	0.41
Ethnic and cultural groups	0.33
Alumni associations	0.5
Environmental groups	0.32
Sports or recreation leagues, whether for yourself or your child		0.54
Parent group or organizations, such as the PTA or local parent support group		0.50
Youth groups, such as the scouts, YMCA, or 4-h		0.38
Fantasy sport leagues			0.33
Fan groups for a particular TV show, movie, celebrity, or a musical performer			0.41
A groups for a particular sports team or athlete			0.45
Fan groups for a particular brand, company, or product			0.41

Not all of the original associational participation items loaded on the first three factors and, thus, were not included in the subscales. However, this does not mean that they are unimportant or inconsistent with the general argument advanced here and elsewhere that associational participation is one important element of social capital. Thus, we perform two sets of analyses: using the cumulative Associational Participation Index as well as models using the subscales emerging from the factor analysis. They represent important, but not exhaustive, possible dimensions of organization participation and/or affiliation. Though partial proxies, they are useful insofar as different aspects or types of organization participation and/or affiliation can have different effects on giving behaviors. We label the first factor “choice-based communities.” The factor encompasses 10 of the associational participation survey items.¹ The chosen label “choice-based communities” is consistent with what was outlined by Schervish and Havens (1997) as the distinction between communities where participation is driven primarily by choice or identification and communities where affiliation is more a result of circumstances. The majority of the survey items conform to the label of being “choice-based communities” insofar as none seem to require participation as a condition for membership; though ambiguous in several cases, in general, participation is likely the result of self-identification with a certain interest protected or served by any of these groups. The underlying items represent involvement with a variety of interests that, to a different extent, pursue different causes. For the most part, the items reflect citizen interest groups in various realms (e.g., geographical, cause-related, and so forth), but their unifying characteristic seems to be voluntary participation.

The second factor identified describes groups devoted to the pursuit of a specific interest—in this case children-related community activities. The most likely source of distinction from the items associated with the first factor is the extent of circumstance-mediated (rather than just voluntary) nature of involvement in these associations. As Schervish and Havens demonstrated, many groups are entered into as a result of circumstances, and the example they give almost literally describes the survey items associated with the second factor here: “for instance, parents with school-aged children are automatically put into contact with numerous school, extracurricular and sports programs which offer opportunities to volunteer time and money” (Schervish & Havens, 1997, p. 241). Thus, though undoubtedly the items associated with this factor substantively describe children-oriented community activities, we label the second factor to represent “circumstance-based group membership.” The logic of circumstance-based participation is congruent with the lifestyles of many Americans (especially suburban dwellers) and are structured in such a way that, for many, children-related issues may be one of the main local community participation opportunities and obligations; thus, many people end up involved in them not just by inclination or choice but as a result of location and circumstances (e.g., having young children).

For the third and last factor identified, labeled “consumer interests,” we find no precedent in the social capital literature. Substantively, the items associated with it describe connectivity or organization participation and/or affiliation driven by some consumer interest—shared interest in a sports club, brand, or a product. This is a topic that has not been adequately researched, although of course the fields of marketing and, more generally, sociology, have been aware for some time that in a consumer society products are interests also imbued with emotional qualities and narratives, such that they satisfy certain needs of identity, a narrative, and belonging. The act of consuming a certain item or brand is also the act of procuring a certain identity, and if such consumer interests drive actual organization participation and/or affiliation, then it is interesting to compare the outcomes of such activity with traditionally conceptualized forms of associational participation.

Results

Before estimating the full models incorporating all independent and control variables, we provide comparisons of the likelihood of donating to a charity online by level of associational participation. Table 4 compares the mean values of the Associational Participation Index for people who have and have not donated to a charity online, and Table 5 provides a cross-tabulation between having donated to a charity online and associational participation quartile.

Table 4.

Comparison of the Raw Associational Participation Index and Online Donations.

Group	Observations	M	SE	SD	95% Confidence	Interval
Do you ever use the internet to make a donation to a charity online?
No	1,249	3.8	0.10	3.57	3.61	4.00
Yes	561	5.6	0.16	3.72	5.32	5.94
Combined	1,810	4.4	0.09	3.71	4.20	4.54
Diff.		−1.8	0.18	—	−2.18	−1.46

Table 5.

Comparison of the Associational Participation Index by Quartiles and Online Donations.

	Social Capital Quartile
Do You Ever Use the Internet to Make a Donation to Charity Online?	1st	2nd	3rd	4th	Total
No	184	338	326	162	1,010
	86.8%	72.5%	65.2%	50.0%	67.2
Yes	28	128	174	162	492
	13.2%	27.5%	34.8%	50.0%	32.8
Total	212	466	500	324	1,502
	100%	100%	100%	100%	100%

Respondents who have donated to charity online on average are characterized with a value of Associational Participation Index almost 2 points (or 67%) higher than those who do not donate online (p = .000). Similarly, Table 5 shows a discernible increase in the proportion of respondents who have donated to charity online in the higher quartiles of the distribution. Specifically, while in the 1st quartile of the associational participation distribution only 13.2% have donated to charity online, half of the respondents in the 4th quartile have done so.

In Table 6 we estimate two logistic regressions to ascertain the impact of associational participation on likelihood of online charitable donations, while controlling for demographic variables that may be spuriously related to both giving and associational participation.¹

Table 6.

Logistic Regressions and Associational Participation Index.

	(1)	(2)
	Do You Ever Use the Internet to—Make a Donation to a Charity Online?	Do You Ever Use the Internet to—Make a Donation to a Charity Online?
Associational Participation Index	0.065**	—
Associational Participation Index	(0.028)
Associational Participation Index: 2nd quartile	—	0.433**
Associational Participation Index: 2nd quartile		(0.186)
Associational Participation Index: 3rd quartile	—	0.818***
Associational Participation Index: 3rd quartile		(0.217)
Associational Participation Index: 4th quartile	—	0.821***
Associational Participation Index: 4th quartile		(0.281)
Age	−0.009**	−0.010**
Age	(0.004)	(0.004)
Do you ever use the internet to—use a social networking site like MySpace, Facebook, LinkedIn, etc.?	0.336	0.317
	(0.219)	(0.209)
Interaction term: Associational Participation Index × Using Social Media	0.021	0.022
	(0.034)	(0.030)
Male	−0.341***	−0.343***
Male	(0.124)	(0.124)
White	−0.002	−0.025
White	(0.161)	(0.161)
Family income: US$20,000-US$50,000	0.982***	0.990***
Family income: US$20,000-US$50,000	(0.243)	(0.243)
Family income: US$50,000-US$100,000	1.085***	1.078***
Family income: US$50,000-US$100,000	(0.245)	(0.246)
Family income: US$100,000 or more	1.527***	1.520***
Family income: US$100,000 or more	(0.262)	(0.263)
Has a college degree or higher	0.619***	0.593***
Has a college degree or higher	(0.133)	(0.133)
Lives in rural area	−0.190	−0.185
Lives in rural area	(0.154)	(0.154)
Has children	−0.351**	−0.357**
Has children	(0.145)	(0.146)
Married or living with a domestic partner	−0.166	−0.197
Married or living with a domestic partner	(0.140)	(0.141)
Uses the internet daily	0.931***	0.932***
Uses the internet daily	(0.151)	(0.151)
Constant	−2.467***	−2.578***
Constant	(0.356)	(0.363)
Observations	1,484	1,484
Pseudo R²	0.14	0.15
% correctly classifies	72%	71%

As hypothesized, associational participation positively influences the likelihood of donating to a charity online, in either model specification: A 1-point increase in the associational participation measure is associated with approximately 1 percentage point (keeping all variables constant at their means) increase in likelihood of donating to charity online in Model 1. As shown in Model 2, being in the 2nd quartile of the Associational Participation Index distribution is associated with 9 percentage points higher likelihood of donating relative to respondents in the 1st quartile. The differential is even larger for the 3rd and the 4th quartiles, each associated with 18 percentage points higher likelihood of donating to a charity online compared with the 1st quartile. The impact of being in the 3rd or the 4th quartile is significantly (both economically and statistically) larger than being in the 2nd quartile.

Usage of social media does not appear to influence the likelihood of donating online, nor is there an interaction between associational participation and usage of social media. Age showed a weak negative effect, perhaps an artifact of age-based “digital divide.” Men are less likely to donate online, and so are respondents with children, whereas people with college degrees and higher incomes are more likely to donate online. Marital status and living in a rural area do not appear to influence the likelihood of making a donation online.

Next, in Table 7 we deconstruct the aggregate measure of associational participation into three measures, constructed on the basis of the results of the exploratory factor analysis discussed above. We estimate four models: Model 1 replicates Model 1 from Table 6, except it replaces the composite Associational Participation Index with the three indices corresponding to the three factors identified. Model 2 adds a composite variable, estimating the intensity of involvement of respondents in any of the groups they are active in to control for the tangible efforts expended by respondents in such associations, based on four binary items that describe the level of involvement with organizations a respondent is active in: taking a leadership role, attending meetings, volunteering their time, and contributing money. Model 2 includes a summative scale of these variables, and Model 3 replaces the summative scale with the individual items. Finally, in Model 4 we use a different donation dependent variable as a counterfactual against which to compare the results for “donations to charity online”: whether or not the respondent has contributed money (in any way, through any medium) to a group they are active in (i.e., the general propensity to donate online assessed so far). Correspondingly, the variable “online donations to charity” is included as an independent variable in Model 4.

Table 7.

Logistic Regression and the Factors.

	(1)	(2)	(3)	(4)
	Do You Ever Use the Internet to—Make a Donation to a Charity Online?	Do You Ever Use the Internet to—Make a Donation to a Charity Online?	Do You Ever Use the Internet to—Make a Donation to a Charity Online?	Contributed Money to a Group You Are Active In?
Choice-based interest groups association (Factor 1, standardized)	0.406***	0.393***	0.403***	0.257**
	(0.091)	(0.101)	(0.101)	(0.119)
Circumstance-based children-related community activities participation (Factor 2, standardized)	−0.121*	−0.162**	−0.153*	0.096
	(0.07)	(0.079)	(0.080)	(0.089)
Consumer interest–based group participation (Factor 3, standardized)	−0.063	−0.093	−0.092	0.008
	(0.067)	(0.068)	(0.068)	(0.075)
Age in years	−0.013***	−0.016***	−0.018***	0.017***
Age in years	(0.005)	(0.005)	(0.005)	(0.005)
Do you ever use the internet to—use a social networking site like MySpace, Facebook, LinkedIn, etc.?	0.315	0.221	0.284	−0.714***
	(0.212)	(0.247)	(0.249)	(0.258)
Interaction term: Associational Participation Index × Use of Social Media	0.023	0.032	0.024	0.088**
	(0.032)	(0.036)	(0.036)	(0.045)
Male	−0.236*	−0.190	−0.203	0.199
Male	(0.128)	(0.136)	(0.137)	(0.147)
White	−0.013	0.022	0.011	−0.008
White	(0.161)	(0.172)	(0.173)	(0.181)
Family income: US$20,000-US$50,000	0.973***	0.852***	0.856***	−0.038
Family income: US$20,000-US$50,000	(0.244)	(0.260)	(0.261)	(0.228)
Family income: US$51,000-US$100,000	1.089***	0.953***	0.960***	0.288
Family income: US$51,000-US$100,000	(0.247)	(0.264)	(0.264)	(0.238)
Family income: US$100,000 or more	1.527***	1.315***	1.354***	−0.207
Family income: US$100,000 or more	(0.264)	(0.279)	(0.281)	(0.266)
Has a college degree or higher	0.582***	0.511***	0.510***	0.056
Has a college degree or higher	(0.135)	(0.141)	(0.143)	(0.155)
Lives in a rural area	−0.190	−0.214	−0.186	0.138
Lives in a rural area	(0.156)	(0.165)	(0.166)	(0.174)
Has children	−0.133	−0.073	−0.092	0.090
Has children	(0.163)	(0.175)	(0.177)	(0.186)
Married or living with a domestic partner	−0.178	−0.110	−0.157	0.421***
Married or living with a domestic partner	(0.142)	(0.150)	(0.152)	(0.157)
Uses the internet daily	0.917***	0.911***	0.911***	−0.009
Uses the internet daily	(0.152)	(0.162)	(0.163)	(0.164)
Intensity of involvement in groups scale	—	0.020	—	—
Intensity of involvement in groups scale		(0.053)
Taken a leadership role in a group you are active in	—	—	−0.154	0.315*
Taken a leadership role in a group you are active in			(0.154)	(0.168)
Attended meetings or events for a group you are active in	—	—	0.059	1.317***
Attended meetings or events for a group you are active in			(0.186)	(0.171)
Volunteered your time to a group you are active in	—	—	−0.276*	0.594***
Volunteered your time to a group you are active in			(0.178)	(0.171)
Contributed money to a group you are active in	—	—	0.500***	—
Contributed money to a group you are active in			(0.159)
Do you ever use the internet to—make a donation to a charity online?	—	—	—	0.516***
				(0.159)
Constant	−2.154***	−1.961***	−1.929***	−2.150***
Constant	(0.340)	(0.381)	(0.387)	(0.388)
Observations	1484	1259	1259	1259
Pseudo R²	0.15	0.13	0.14	0.22
Percent correctly classified	71%	70%	70%	75%

Model 1 provides insight into different connections that different components of associational participation have on the propensity to donate online (Table 7). The only associational participation subscale that has a positive significant effect on the likelihood of donating online is the “choice-based group participation” in community or interest groups. “Circumstance based,” children-related community activities had a negative, marginally statistically significant, impact on likelihood to donate, whereas “consumer interest–based” participation in associations/fan club activities had no effect. Adding the summative index of intensity of involvement in groups in Model 2 does not appear to be significant as a general variable, and Model 3—where all four types of direct involvement are featured as separate variables—reveals interesting distinctions: Although taking a leadership role in a group the respondent is active in and attending meetings has no discernible impact on the likelihood of online donations, volunteering one’s time to a group has a marginally significant negative impact on the likelihood of donating online. On the contrary, contributing money to a group in which one is active is apparently also positively associated with a general propensity to donate to a charity online.

Model 4 uses a different dependent variable—donating specifically to an organization the respondent is active in, through any medium. The distinction between contributions directly to an organization one is actually active in and a general online donation is meaningful, since it provides a counterfactual to the claim that associational participation increases general involvement in variety of causes, not just involvement directly with the organizations that are the source of such associational participation. First, the effects of the associational participation factors are virtually identical with the models using general donation online as a dependent variable: Only choice-based participation in groups is significantly and positively related to the likelihood of contributing money to a group one is active in. Participation in circumstance-based (children-related) groups, though marginally significant in the preceding models, appears to have no discernible effect. Interestingly, the use of social media has a negative and significant effect, though it is partially discounted by higher levels of associational participation (the interaction term between social media usage and likelihood of contributing is positive and significant).

Neither income nor education any longer have an effect on the likelihood of contributing to a group one is active in (unlike the likelihood of making a donation online). Another distinction in Model 4, being married, is positively and significantly associated with the likelihood of contributing to an organization one is directly involved in, whereas daily usage of the internet is not. All measures of intensity of involvement with groups (leadership roles, volunteering, attending meetings) are positively and significantly correlated with the likelihood of contributing monetarily (with the possible exception of taking a leadership role, marginally significant at the 10% level).

Discussion and Conclusions

This article examined the impact of participation in voluntary associations on the propensity of individuals to make donations online to charities. The results support the general hypothesis that higher levels of associational participation (approximates as the sum total of organization participation and/or affiliation of an individual, that is, his or her involvement or participation in a wide range of groups and organizations) will increase the likelihood of contributing to various causes or charities. A possible theoretical explanation for such a connection is the role of social capital as a driver or consequence of organization participation and/or affiliation. Social capital is a complex concept, and the focus here is specifically on the conceptualization of social capital as the totality of linkages between individuals and their associations, and the resulting inclinations toward civic engagement. In this study, retaining this possible mechanism, we proposed to test a direct connection between organization participation and/or affiliation and charitable giving. Though not directly tested, the empirically confirmed relationship between organization participation and/or affiliation and charitable giving is congruent with a process possibly mediated by social capital, including several qualifications. Generally, the implied mechanism being that greater levels of organization participation and/or affiliation (i.e., representing one aspect of social capital) means greater involvement with one’s social context, and therefore, greater degree of involvement and concern with issues in this context. This involvement—or “identification”—(Schervish & Havens, 1997) is understood as an important determinant of giving. If identification is the product of “encounters” and “relationships” (Schervish & Havens, 1997), then a direct connection can be established by the extent of relational involvement and acts of giving resulting from identification with and inclination to address various causes. Since nonprofits and charities are one of the few accessible civic means for directly addressing perceived problems, there should be a direct connection between higher levels of connectedness and giving, as this study empirically demonstrates, controlling for other sociodemographic factors. By any measure, individuals with greater levels of associational participation were more likely to donate to charities online or to contribute monetarily (through any medium) to organizations they are directly involved with. This result qualifies previous findings (e.g., Scharvish & Havens, 1997) by showing that the mechanism of “identification’ with causes likely extends past organizations and relationships individuals are directly involved with.

With the general expectation supported empirically, there are several important distinctions and qualifications that have practical and theoretical significance. First, we find that different dimensions of organization participation and/or affiliation (e.g., choice-based interest group participation—local or nonlocal, circumstance based—typically local, children-oriented associations, and consumer interests/fan clubs) have varied effects on propensity to donate online. The only subscale of organization participation and/or affiliation that positively affected the likelihood of donating online is choice-based interest group participation, whereas involvement in circumstance-based children-oriented local activities had a negative effect (and consumer interests–based group participation had no discernible effect).

Second, we find a distinction between the determinants of online charitable giving (which can be made to any organization, any cause, whether the respondent is involved with them in any other way or not) and contributing (through any medium) to groups and organizations in which the respondent is directly involved. The link between group involvement is most directly visible in the activities and donation behavior pertaining to organizations individuals are directly involved with: The greater the involvement through any means (e.g., volunteering, meeting attendance, leadership), the greater the likelihood of monetary contribution, thereby strengthening Scharvish and Haven’s (1997) explanation of giving as the result of identification, mediated by relationships and encounters. Although direct involvement may in some aspects compete with broader involvement and general charitable giving, the actual sum of associational activities and the resulting inclinations does increase general propensity to donate.

Specifically, the intensity, or the nature of involvement in groups had distinct effects on the propensity to donate. First, taking on leadership roles or attending meetings had no discernible effect. People with higher overall propensity to contribute money to organizations they are active in are not, surprisingly, also more likely to contribute to charity online. However, there is a potential trade-off between contributing time and effort to one’s groups (e.g., through volunteering) and propensity to donate to charities online. This result is in line with the possible distinction between choice-based and circumstance-based participations (and also likely between localized and nonlocalized); since actual volunteering effort most often requires physical proximity, it reinforces the finding that individuals whose social links are such that they are primarily concerned with contributing to localized causes and are less inclined to donate online to charities in general. Existing research argues that rather than merely emphasizing membership in groups as an outcome for citizen engagement new research should address more individual involvement in public policy (Suarez, 2009).

It is interesting that usage of social media negatively affects the propensity to contribute to organizations (with which the individual is directly involved with), although higher levels of organization participation and/or affiliation diminishes this negative effect. This result is potentially important for improving our understanding of how social media interacts with social relationships and online behaviors because it suggests that heavy use of social media might indeed be associated with diminishing (localized) organization participation and/or affiliation; however, higher levels of organization participation and/or affiliation, if present, may be reinforced by the usage of social media.

Just as interestingly, there was no apparent relationship between whether someone engaged in social media activities, such as Facebook and Twitter, and online donations, and future research needs to understand what strategies and campaigns that may be able to take advantage of this medium. Considering that choice-based organization participation and/or affiliation was associated with online charitable giving, it appears that better application of social media should be one of the primary goals of charities or groups who pursue more general causes insofar as they have limited other channels to generate involvement, unless they also have localized opportunities for participation and involvement. Greater technological sophistication alone does not lead to increased online donations, unless driven by broader, including offline, involvement in social and group activities. The internet is a likely mechanism that appears to amplify the effects of existing social capital on the likelihood of donating to organizations individuals are directly involved, or “identified,” with. A challenge for nonprofits relying primarily on the internet for donations is to devise strategies to foster individual identification with causes through other means.

This result is consistent with the existing literature on social capital and internet behavior, showing that being more technologically sophisticated does not necessarily imply having greater degrees of social capital (Nie, 2001), instead of showing that more traditional behaviors provided the best explanation of donations to online charities. In other words, if “identification” and social capital are present, the internet may catalyze action. The internet provides easier opportunities for forming connections, but it does not provide the content or the inclinations underlying these connections, and hence, our result that online charitable donations are primarily the result of preexisting organization participation and/or affiliation rather than the availability of technology. No doubt charities need to be responsive to the newest technological trends such as social media, but they must recognize that it is critical for them to understand the substantive underpinning of associational behavior and activities.

There are some limitations to this study, with the most important being that the data set used was not specifically geared toward surveying citizens and their willingness to donate online. Using a preexisting survey that focused on the internet and civic engagement eliminated in our analysis some important factors identified in the donor behavior literature. Therefore, we were more restricted in the type of variables that we could use to measure organization participation and/or affiliation and online donations. Another limitation is that because of the very fast changes to internet technology, the results presented in this study may be outdated given the fast pace of technological change.

For general online donations, our results are suggestive that a social media or online marketing strategy that targets respondents with preexisting high involvement in various groups, associations, and causes may be beneficial. From this rationale and results, it appears that greater organization participation and/or affiliation may result in greater concern for a variety of causes and issues, and thus, greater willingness to contribute monetarily. This suggests that online donation campaigns may need to investigate different means to promote feelings of actual identification with certain causes, that is, providing opportunities for actual meaningful relationships for individuals, rather than merely opportunities to donate in response to marketing campaigns. Such opportunities need to appeal to, and provide, opportunities for meaningful contributions (beyond simply donating money), likely by emphasizing some social benefit resulting from relational involvement.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

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

Notes

Author Biographies

Christopher G. Reddick is an associate professor and Chair of the Department of Public Administration at the University of Texas at San Antonio, USA. His research and teaching interests are in public budgeting and information technology in public sector organizations. Some of his publications can be found in Government Information Quarterly, Electronic Government, the International Journal of Electronic Government Research, and Public Budgeting & Finance. His recently edited the two-volume book entitled Handbook of Research on Strategies for Local E-Government Adoption and Implementation: Comparative Studies. He is also author of the book Homeland Security Preparedness and Information Systems, which deals with the impact of information technology on homeland security preparedness.

Branco Ponomariov is an assistant professor in the Department of Public Administration at the University of Texas at San Antonio, USA. His research interests are in the fields of public management and science and technology policy. His work appears in journals such as Research Policy, Journal of Technology Transfer, and Public Administration Review.

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