Social Media and Mobile Fundraising for Haiti: A Behavioral Economics Perspective
March 2nd, 2010 § 3 Comments
Accompanying Prezi can be viewed here.
Much has been written about the success of mobile giving following the devastating earthquake in Haiti on January 12, 2010. The Red Cross has received considerable praise for its partnership with mGive. To date, mGive has processed over $37 million in donations to Haiti according to the Denver-based nonprofit’s blog. This far surpasses previous mobile fundraising efforts following natural disasters, including the $190,000 the Red Cross raised through mobile giving after Hurricane Ike in 2008.
The convenience of donating through mGive was an obvious boon to the Red Cross fundraising effort: A donor simply texts the word “Haiti” in a cellphone text message to the number 90999, which automatically adds a $10 pledge to their phone bill. As mGive’s website claims, donations can be completed within 10 seconds. And beyond convenience, mGive effectively reframed and decoupled the transaction. Since the loss of $10 would be realized at a later date and was bundled with the mobile phone bill (a service that most people consider an indispensable utility), the transaction didn’t count as a charity withdrawal in one’s schema of mental accounts.
But why did mobile giving catch on this time around?
With the loss of over 230,000 lives, the human toll resulting from the Haitian earthquake ranks it the deadliest natural disaster of the past century occurring in the Western Hemisphere. Undoubtedly, this magnitude of human loss and suffering helped spur donations. Couple this sobering reality with endorsements from the White House and various celebrities and you can easily see why the Red Cross partnership with mGive was deemed a legitimate and worthwhile cause.
As I’ve illustrated in the Prezi above, social networking sites also fueled the success of the Red Cross campaign. Within hours of the earthquake, the Red Cross tweeted: You can text “HAITI” to 90999 to donate $10 to Red Cross relief efforts in #haiti.”
This text meme quickly took root in social networks. According to web analytics firm Sysomos:
There were 2.3 million tweets about “Haiti” or the “Red Cross” from Jan. 12 to Jan. 14, and nearly 150,000 tweets that included “Haiti” and “Red Cross.” Of the 2.3 million tweets, 59% were retweets. There were also 189,024 tweets that included “90999.”
This is clearly an unprecedented case of the effects of social media on fundraising. But in addition to rapidly getting the word out, I would argue that social media also played a significant role in fueling what behavioral economists call “image motivation.” Image motivation refers to an individual’s tendency to be motivated by how others perceive them. Applied to altruism, this phenomenon explains the social currency of “looking good by doing good.”
Social Media, Image Motivation, and Giving
In their 2007 paper titled, “Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially,” Dan Ariely, Anat Bracha, and Stephan Meier examine the effects of image motivation and extrinsic rewards on giving. They find that the desire for social approval means that:
Conditional on prosocial activity yielding a positive image, people will act more generously and prosocially in public than in private settings.
We can assume that in the case of the Red Cross mobile giving campaign, image motivation was perpetuated through social media channels like Facebook and Twitter. Tweets and status updates that spread the word about texting “HAITI to 90999” more than served the purpose of notifying others; these short strings of text provided a means of image motivation. As word of the cause quickly disseminated from social media influencers to the masses, donating to the cause became an established norm within social networks. Social reputation was now at stake.
Ariely, Bracha, and Meier demonstrate that in addition to improving participation, image motivation in social settings also increases the amount people donate. They conducted an experiment that simulated making a donation to the American Red Cross through a combination of keystrokes in a software program. Participants were randomly assigned to one of two groups: (1) A private group, where the amount donated was only known by the participant and (2) A public group, where the participant had to publicly reveal to other participants what they donated. Participants in the public group donated significantly more to the Red Cross: the average number of clicks, at 900, was nearly double the average of 517 clicks for participants assigned to the private group. (Ariely, Bracha, and Meier then go on to investigate the interplay between extrinsic rewards and image motivation.)
What might this teach us about future fundraising efforts?
In their 2004 paper, “Public goods experiments without confidentiality: a glimpse into fund-raising,” James Andreoni and Ragan Petrie examine the group dynamics of image motivation in fundraising. Andreoni and Petrie found that:
Identity and information can matter. Knowing only the distribution of contributions, but not the identity of the givers, has no discernible effect. Knowing only the identity but not the individual contributions has a modest effect of increasing donations. However, knowing both who is in your group and what each is choosing [to donate] can significantly increase giving . . . the combination of information and identification tends to increase contributions. Information and identification together result in 59 percent more giving to the public good over the baseline of the typical public goods experiment.
Along with Ariely, Bracha, and Meier’s findings above, this implies that fundraising efforts that make use of social media benefit from making it easy for donors to share the amount they contributed with their social network friends and followers. In the case of the Red Cross campaign for Haiti, $10 provided an agreeable anchor point that allowed people to give within their means — skip Starbucks for two days and you’ve made up the loss. Without this anchor point, the mGive transaction would have lacked a social norm within social networks and may not have been passed along to the degree it was. I wonder what this might have looked like had people been able to broadcast how much they gave above and beyond the $10 anchor point, or alternatively, how many people they shared news of the cause with.
I think what we can learn from this is that it is incumbent on fundraisers to understand the principles of image motivation within social networks when organizing efforts that utilize social media. Further, as mobile fundraising continues to gain momentum, fundraisers should look to smartphone apps like Causeworld and The Extroardinaries for a glimpse as to where mobile-social giving is headed. These apps feature novel approaches toward signaling image motivation in social networks while on the go.
The Buzz about Google Buzz: Behavioral Economics and Online Privacy
February 14th, 2010 § Leave a Comment
Last week Google launched Google Buzz, a new status update and social network aggregation feature for Gmail users. Within minutes of its launch, digerati took to Twitter and blogs to discuss their first impressions of Buzz. Early reviews were generally positive, but my mid-week, the tone had shifted dramatically. It began to surface that Buzz had a fatal privacy flaw that could potentially jeopardize the real-world safety its early adopters. As one widely shared op-ed piece on Gizmodo.com explained, “Fuck you, Google. My privacy concerns are not trite. They are linked to my actual physical safety . . . You have destroyed over ten years of my goodwill and adoration.”
Fueling this vitriol was a naive design decision to eliminate signup procedures allowing users to choose with whom they would like to share information. As the Google Buzz landing page describes, “No setup needed. Automatically follow the people you email and chat with the most in Gmail.” (In the case of the writer above, her abusive ex-husband could now view her status updates and items she shared with her current boyfriend.)
This incident underscores the importance of understanding the behavioral economics of privacy in online settings. Had Google designed buzz to better account for how users make decisions as to the degree of personal information they feel comfortable sharing in initiating a service, this scenario could have been avoided.
Understanding behavioral economics to design for privacy
In Alessandro Acquisti and Jens Grossklags’ 2006 paper, “What Can Behavioral Economics Teach Us About Privacy?,” the authors address the complex and often contradictory behaviors surrounding online privacy. As Acquisti and Grossklags describe, “we feel entitled to protection of information about ourselves that we do not control, yet willingly trade away the same information for small rewards; we worry about privacy invasions of little significance, yet overlook those that may cause significant damages.” The authors explore the behavioral principles that impair or aid individuals in making privacy decisions online, concluding that behavioral economics can improve privacy policy decision making and technology design for end users and data holding entities.
The case of Google Buzz is a clear example of asymmetry of information in decision making. In opting in to the service, users had no way of understanding the algorithm Google was using to automatically link contacts nor the extent of data that would be shared between contacts (at least at the outset). Users were not guided through procedures at signup that would make this clear.
In addition, users were subject to status quo bias due to the simplicity of opting in to the service. To make signup simple, Google intentionally made the “automatic follow” feature a default setting. Thus, many users failed to realize what and with whom they were sharing information until hours or even days after signup. This agrees with Acquisti and Grossklags’ findings, whereby their study of online social networks revealed that the vast majority of users do not change their default (and very permeable) privacy settings (likely due to status quo bias).
But why all the vitriol? While the personal safety violation described above represents an extreme case, negative opinions of Google Buzz circulated widely. It is widely known how Google benefits from the personal data of users of iGoogle and Gmail. In the case of Buzz, Google was looking to capitalize on real-time social data by stealing users from other popular social networking services like Twitter and Facebook. But as Facebook has learned from its own privacy issues, the fungibility of user data and free services is a complex and sensitive issue. Acquisti and Grossklags found the behavioral economics of inequity aversion to be at play when users make decisions to share their personal data: “In the privacy arena, it is possible that individuals are particularly sensitive to privacy invasions of companies when they feel companies are unfairly gaining from the use of their personal data, without offering adequate consideration to the individual.” With Google Buzz, it’s likely the case that users felt they were receiving relatively little in the way of new functionality (beyond what services like Facebook and Twitter currently offer), in exchange for automatically (and often unknowingly) giving up personal and social data. Hence, a sense of privacy violation is apparent in much of the negative backlash.
Privacy principles to consider in future design projects
Setting a user’s expectations of what and with whom their personal information is shared is clearly a critical step in developing a service. And in most cases, it’s not enough to provide a privacy policy at signup. Often, the economic principle of rational ignorance comes into play. As posited by Acquisti and Grossklags:
Ignorance can be considered rational when the cost of learning about a situation enough to inform a rational decision would be higher than the potential benefit one may derive from that decision. Individuals may avoid assessing their privacy risks for similar reasons: for instance, they may disregard reading a data holder’s privacy policy as they believe that the time cost associated with inspecting the notice would not be compensated by the expected benefit.
Privacy policies, often lengthy and rife with legalese, are generally not designed to give online service users greater understanding of the tradeoffs or probability of risks associated with opting in to a service or service feature.
As designers, we must also consider what Google learned from crossing two distinct modes of online social interaction. While Google stood to gain traction quickly by offering Google Buzz to its large installed base of Gmail users, crossing the modes of email and social networking through the “automatic follow” default was shortsighted. Users share different types of information through these two channels and communicate with different degrees of intimacy. This teaches us that when developing services that combine multiple modes of online communication, we need a clear picture of the behavioral norms of each of the channels incorporated into the design.
Streams with banks vs. Waves without shores
October 15th, 2009 § 1 Comment
Many in the tech press are wondering whether Google Wave will achieve mass adoption. And while I haven’t yet been granted a golden ticket to try the beta, I’m already skeptical of whether this platform will find a place in my lifestream. I think the biggest issue I’ll have with Wave is that it strives to be synchronous while at the same time having few design constraints. Let me explain . . .
I find utility in Twitter, Yammer, and Facebook as both synchronous and asynchronous platforms. At times, I’m actively engaged in streams of data from these services. At other points in time, I momentarily dip into the stream or receive push notifications of certain types of information. And all three of these platforms have inherent technical or behavioral constraints, which is actually what makes them so useful: Throughout the day, I snack on bite-sized Tweets and bit.ly links from people involved in my interests. At work, I tap into Yammer to get a brief glimpse of what colleagues are tackling. And Facebook, while not as constrained as Twitter, provides me with a ready stream of social snacking. All three of these platforms combined with MMS, Skype, and good old telephony are always at hand with my iPhone.
So while constraints have helped make platforms like Twitter useful for me, Google Wave’s lack of constraints and demand for synchronicity may ultimately make it useless. Lev Grossman said it well in his recent review of Wave:
Wave operates in real time, it demands immediate attention like an IM or a phone call, or for that matter, a crying baby. When Wave is up, it’s hard to focus on anything else. That isn’t a defect, but it does narrow the scope of its usefulness. Getting more information right away isn’t always the most efficient way to work.
I suppose only time and experience will tell whether Wave is a useless firehose of distraction or a useful collaboration and aggregation platform. So I best get back to finding myself one of those golden tickets.
When will Facebook give us an iPhone app that does something remarkable? Here’s one idea.
September 4th, 2009 § Leave a Comment
Last week, Facebook released the latest iteration of their popular iPhone app. While the user experience is significantly improved, the Facebook app sadly remains little more than a miniaturized version of the Facebook website. This “lazy” approach to mobile app development certainly isn’t unique to Facebook. iTunes is littered with apps that merely mimic consumer websites, thus failing to offer brand experiences particular to the iPhone and its unique modes of use. (One notable exception is the Amazon iPhone app, which offers Amazon Remembers, an assisted shopping service specifically designed for mobility and iPhone functionality.)
What’s perhaps most disappointing about Facebook’s approach is that they have been slow to develop new services that take advantage of the wealth of in situ user-reported data about our activities, moods, and behaviors.
Consider Facebook’s now dominant role as a photo-sharing site. At its busiest, Facebook loads 550,000 photos each second, and you can assume that a fair share of these photos are uploaded or viewed via Facebook’s apps for smartphones. Now consider the metadata associated with these photos: The user’s GPS location, compass orientation, the time and date the photo was taken, whom else is present (via photo friend tags), and associated captions and concurrent status updates that provide some semantic cues as to what the photo literally and emotionally represents to the end user.
Get the picture?
The Facebook mobile app, and more specifically, the Facebook iPhone app is a powerful generative platform for an entire range of new services that Facebook could (and frankly, should) offer.
Imagine using Facebook’s deep archive of profile data and in situ metadata to find places and events around town that fit your desires at a moment’s notice. Imagine having the ability to automatically view photos from your profile and the profiles of friends associated with a given location, time, or mood (“Placebooks,” anyone?).
To show you what this might look like, here is a very preliminary Facebook mobile app concept called “Sugar” I developed with two of my colleagues from the Institute of Design:
So where is mobile social networking heading? And what may lie ahead for Facebook?
Loopt, a location-based social network certainly grasps the power of in situ real-time user data and has recently begun offering “always on” service for users, allowing them to be alerted of the activity of nearby friends. While it is yet to be seen whether this type of service will achieve wide-scale adoption, it is an indication of what Facebook should be considering in future iterations of its mobile app (with the proper privacy features, of course). And to be fair, it seems Facebook may already be exploring such an idea with Nokia.
Mobile is the future of Facebook. When will Facebook begin to fully grasp this?
