Abstract
Social media outlets provide nonprofit organizations the opportunity of opening new communication and disclosure channels. Organizations must decide whether to set up these channels. They—and in turn their target audiences—must also decide how much to use social media. In this study, we test a novel multi-level signaling theory framework to examine the relationship between social media investments and financial returns. Employing both cross-sectional and cross-temporal samples of 427 of the largest US nonhospital charities, we examine the association between donations and three dimensions of organizations’ social media efforts: (1) whether the organization has a social media presence, (2) how much the organization uses social media, and (3) the level of engagement of the organization’s audience. The findings support our conjecture that financial returns result from establishing a particular communication channel, from using that channel, and from having channel-specific audience engagement. We also consider how our three social media signaling dimensions condition the core donations demand variables, finding that social media substitutes for traditional fundraising expenditures. These results carry implications for the signaling and donation demand literatures and further the understanding of how new media are changing donor engagement.
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Notes
The chief investment is concentrated in the personnel cost to initiate and maintain a social media presence. The cost of personnel can be classified as fundraising expense, program expense, or management and general expense on the Form 990. Since most social media presence involves a discussion of programmatic activity and broad stakeholder engagement efforts rather than direct fundraising (e.g., Guo and Saxton 2014; Waters 2011), we expect only a small portion of social media investment to be classified as fundraising expenses. We confirmed this expectation with a partner at a local CPA firm specializing in nonprofits, who noted that staff time dedicated to social media would typically be allocated between program expenses and administrative expenses. The exception would be a development person using social media as a part of their time spent on fundraising.
For example, donors who are prompted to give by the organization’s social media communication may be targeted by the organization’s other fundraising venues, such as websites, email, mailouts, or in-person fundraisers. In that scenario, the social media efforts are not capturing any new donations. In a worst-case scenario, those same donors could decide to give less when they are motivated to give from social media than when they are spurred to give by an in-person meeting, a gala fundraiser, or an email. We also note our model is not concerned with the specific vehicle through which money is donated; in our explanation, it does not matter whether the donor gives through social media or via a third-party app, a website, a text, social media, or in person. Money donated through all sources will show up in our empirical donations dependent variable.
The accounting literature explaining donations generally incorporates one or more new variables into the baseline donations demand model, thereby testing the incremental effect of such concepts as third-party ratings (Gordon et al. 2009), accounting information quality (Yetman and Yetman 2013), internal control deficiencies (Petrovits et al. 2011), or CEO pay (Balsam and Harris 2014).
While our arguments are at the aggregate, organization-year level, it may be useful to also see how at the message level organizations can tap into specific donor preferences. An example of a Facebook message targeting impact maximizers is the following, sent by the Institute of International Education on October 8, 2015: “Have you had the chance to look at the one year impact report for #GenerationStudyAbroad? Here’s a quick rundown of key things to know about the initiative and our 600 + commitment partners.” The message was accompanied by an infographic with key impact indicators. Nonnumerical examples of such messages typically show examples, data, or photos of the organization’s charitable output, programs, or impacts.
An example of a message relevant for status-maximizers is the following, sent by the Carle Center for Philanthropy on April 28, 2016, acknowledging a large donor: “There’s no better way to say ‘thank you’ to our donors than with a surprise visit from some of their biggest fans! Students from Carle Auditory Oral School hopped on a bus this morning to deliver a special message of thanks to First Federal Savings Bank and GTPS Insurance Agency, the VIP sponsors of INSYNC. This premier lip-syncing contest raised more than $40,000 for Carle Auditory Oral School, and the students are giddy about everything these two businesses have made possible for their school.” As in this sample post, such messages often include a photo of the donor along with some acknowledgement or recognition.
For example, the following post, sent by UNICEF on January 13, 2010, carries relevance for relationship-maximizers in how the message stresses community and relationships rather than societal impact or personal status: “Help us help Haiti! Create a personal fundraising page and share it with your friends and family asking them to donate. Part of a group, school or company? Create a team page and the reach and impact become even greater.”
Consistent with the term “countersignaling,” the audience’s actions are both a response to specific organizational messages (and thus countersignals) and, collectively, signals themselves of donor-relevant organizational characteristics. In related research, capital markets studies have considered user-generated “cashtag” tweets to be indicators of information content, investor attention, and aggregate opinion (Bartov et al. 2018; Curtis et al. 2016; Lee et al. 2015). While our “countersignals” are similarly a form of user-generated content, they are distinct in that they are linked to specific organizational messages.
Also relevant here is how a donor’s countersignaling engagement provides the vehicle for identity signaling and virtue signaling practices (Wallace et al. 2020): each time a donor engages by sharing, replying to, or liking a nonprofit organization’s message, the individual has another opportunity to signal a prosocial identity intended to be seen favorably by others; and the larger the countersignaling network, the more salient the status-boosting signal.
We focus on the number of messages and countersignals to test H2 and H3, given that we are interested in the amount of effort an organization puts into its social media presence as well as the extent of the constituent response to this effort. For H2, employing a measure of message frequency is supported by findings of the signaling power of the analogous measure of advertising intensity (Aiken and Boush 2006), by evidence of the resource-acquisition effects of the frequency of social interactions (Fischer and Reuber 2011), and by findings that the amount of firm-generated content drives financial value (Mumi et al. 2019). Similarly, for H3, the use of a volume measure of countersignaling is supported by the electronic word-of-mouth literature, which has substantial research suggesting that volume-based measures are more important drivers of consumer behavior than content-based measures such as sentiment/valence (Amblee and Bui 2011; Cheung et al. 2014). Given this evidence and in line with our hypotheses, we focus on volume and leave content/valence to future research.
Direct donations are those received from individual donors, while indirect donations are those received through third-party administrative organizations such as The United Way. Alternatively, we considered measuring the dependent variable as either government grants or program service revenues. However, we expect donors to be relatively more sensitive to social media usage, compared with governmental entities or stakeholders engaging nonprofits in a fee-for-service arrangement.
Model results are robust to alternatively including contemporaneous Fundraising Expenses or both lagged and contemporaneous Fundraising Expenses. We use the lagged version in our main model following prior literature.
The Form 990 is due on the 15th day of the fifth month after the organization’s year-end. The IRS allows nonprofits to extend the 990 filing deadline an additional six months. There is also a notable time lag between when a nonprofit eventually files the Form 990 and when the IRS creates and disseminates data files such as the SOI files. The result is that at the time of our study (2016) the most recent SOI file was for 2013.
The coefficient on a dummy variable where the dependent variable is logged can be interpreted as 100[exp(c)—1] where C is the coefficient estimate (Halvorsen and Palmquist 1980).
2008 is the first year organizations could create a Facebook page. Our time-series sample begins in 2009 on account of the lagged variables in our empirical model.
We collect digitized Facebook data in October 2016 by using custom Python code to access the Facebook application programming interface (API). Specifically, we used the API to download all organizational messages along with the number of audience likes, shares, and comments on each organizational message. For example, for an organization that created its Facebook page in 2008 (the first year pages were implemented by Facebook), we downloaded all posts made from 2008 through 2016, along with counts of the numbers of shares, likes, and comments for each organizational message. Similarly, an organization that set up its Facebook page in 2010 would have 2010 through 2016 data on the number of posts as well as audience likes, shares, and comments. We then aggregated these data by organization/year to obtain annual counts of the number of organizational messages sent as well as the number of audience reactions (countersignals) for each organization/year. Note that the number of Facebook followers held by an organization may drive audience reactions (Saxton and Waters 2014). Yet given that an individual can like, comment, or share an organization’s message without following the organization, the number of followers would provide only an indirect measure of audience engagement. In effect, our measures of audience likes, comments, and shares offer a more direct measure of message-driven countersignaling and audience engagement and thus are best suited for our tests. For this reason, we do not collect data on the number of Facebook followers.
Our countersignaling models are also robust to including Messages as an additional control variable.
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Acknowledgements
We thank Steve Balsam, Chao Guo, and workshop participants at Drexel University, York University, and Rutgers University for helpful comments. Villanova School of Business MBA fellows provided research assistance.
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Harris, E.E., Neely, D.G. & Saxton, G.D. Social media, signaling, and donations: testing the financial returns on nonprofits’ social media investment. Rev Account Stud 28, 658–688 (2023). https://doi.org/10.1007/s11142-021-09651-3
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DOI: https://doi.org/10.1007/s11142-021-09651-3