This article was written by Tyler Whittle, DMG and Research Lead at DAO Masters and co-creator of Outliers. Thanks to Ron Tidhar, Rob Bremner, Nelson Jordan for their insightful comments and feedback. And a huge shoutout to the entire DAO Masters community for being awesome :)
So you’ve got the first core contributors of your DAO and the community seems to be up and running. Congratulations! Jumpstarting a DAO is no easy feat. However, once you’re there, the challenges of successfully running a DAO have just begun…
The upside of a community is that its permeable organizational boundaries allow for lightning fast scaling. New members are attracted by the mission and there is minimal friction to joining and contributing. This is what allows DAOs to achieve such massive production in fractions of the time it would take traditional corporations.
However, permeable organizational boundaries are a double-edged sword. While it’s easy to join a DAO, it’s just as easy to leave.
Why would members choose to leave a DAO? Well, while DAOs initially attract members with an exciting and bold mission, they often engage, retain, and reward contributors with compensation. Yet compensation is a thorny issue. Perception of fairness matters. A LOT.
Rather than spend paragraphs convincing you fairness is important, I’ll ask you to watch this quick video on a canonical study in fairness.
This highlights a core tension DAOs face when balancing elements of a corporation and a community: Like a community, DAOs can attract members and quickly scale by leveraging a bold and exciting mission. But like a corporation, DAOs often engage, retain, and reward contributors with compensation.
Balancing the mission and the money is critical to a DAO’s success.
To help DAOs better manage this unique challenge, I’m going to introduce a framework derived from organizational psychology called the DAO API. By using the DAO API, DAOs should find that their members are more satisfied and stick around longer — ultimately allowing the DAO itself to perform better.
Just like an API defines how a focal application can connect and interface with many other applications, the DAO API defines how a DAO can best connect and interface with its myriad of unique members.
The DAO API is predicated on three different principles of fairness, each of which has been shown to predict satisfaction, retention, and ultimately organizational performance. These three principles are:
To help illustrate the power of this framework, the three pillars of the DAO API have been shown to predict employee satisfaction. Employee satisfaction, in turn, predicts organizational performance. For example, companies listed in the “100 Best Companies to Work For in America” generated 2.3% to 3.8% higher stock returns per year than their peers from 1984 through 2011 (Edmans, 2011).
Think about the compensation process — one of the most challenging, but also one of the most critical, aspects of managing a DAO. Below I’ll explore how each component of the DAO API applies and provide actionable recommendations for improving perceptions of fairness.
Allocation. Whenever a contributor is compensated for their work, they will inevitably ask themselves: “Do I feel like this reward met my expectation given the value I created?” The answer determines their perception of fairness with respect to allocation. If the answer is yes, then that contributor is going to have a positive perception of their allocation.
Unfortunately, this will often not be the case for a variety of reasons. Individuals have their own intrinsic aspiration levels that they expect to be met. Along with this, they will base perceptions of fairness on observations of peers’ compensation within their organization and across other similar organizations.
Recommendation: One of the best ways to improve the perception of allocation fairness (A) is by communicating expectations for compensation at the beginning of a season or project. The most straightforward way of doing this is a bounty. If a DAO promises to pay $500 for a particular amount of work, then a potential contributor can assess in advance whether that price seems fair.
When bounties don’t make sense, and they often don’t, many DAOs have chosen to determine pay after work has been done. Some great ways to preempt compensation expectations in this case is through pre-allocating team budgets or providing examples of past payouts.
For instance, you could inform your project leads at the start of a season that project leads have received roughly 30–40% of the allocation for their respective team’s budget in past payment processes. If a project lead knows their team’s budget this season is 2 ETH, then they have a reasonable reference point for an expected payout.
Process. In traditional organizations, employees are generally compensated in one of three ways:
What is true across each of these types of compensation is that the procedure for determining the employees compensation is generally straightforward. Salary is a predetermined annual rate, and bonus is based on some combination of individual, team, and company performance. Hourly wage is, of course, a linear function of time spent on the job.
In practice, equity can be a little bit more complicated. However, equity allocations have been simplified by heuristics arising from startup founders mimicking what is considered “best practice”. In DAOs, compensation often operates like an equity split. But today, “best practice” is at best not broadly understood, and perhaps even ill-defined.
Right now, many DAOs are relying on tools like Coordinape or SourceCred for their compensation. For example, Coordinape runs in epochs where at the end of an epoch, DAO members allocate 100 GIVE tokens to their teammates. A member’s total compensation is based on the number of GIVE tokens they receive relative to the total supply allocated.
However, the process that each DAO uses (and within each DAO, the process used by individual members) to allocate rewards with these tools varies widely. We saw four distinct logics emerge in DAO Master’s first payment using Coordinape. Check out DAO Master’s full review of our first Coordinape experience to get an in-depth look. The problem is, this high degree of variance in process increases the likelihood of perceived (and actual) unfairness.
Lastly, I’ll note that process fairness can work in concert with allocation fairness. If a contributor felt like they should have been paid more, but that the process for determining their compensation was fair, then they will be less likely to churn. Thus, not only is it important to ensure contributors feel fairly compensated, but also that they felt the process for allocating the rewards was fair.
Recommendation: One way to improve the process fairness (P) is requiring all contributors to submit their logic for allocations and publish that logic publicly after everyone’s payment has been determined. This achieves two things. First, writing out the logic for allocation forces people to actually reflect on what each members’ contributions were. This helps remove recency bias, diminish the likelihood of a popularity contest, and anchor people on the actual value that others provided. Second, publicly publishing allocation logic strongly deters bad actors in the ecosystem. It’s much harder to send 90 of your 100 tokens to your friend when you have to justify that allocation to the rest of the community. Finally, over time this lets the community converge on logics that they like and perceive to be most fair.
Interaction. Finally, the way that a DAO interacts with its contributors throughout the compensation cycle is critical for perceptions of interaction fairness (I). Since the compensation process relies on subjective measurement of value contributed, it is important to ensure that each member feels treated with respect and dignity throughout this process. While interaction fairness is one of the easiest to viscerally understand, it can be the hardest of the three to consistently implement.
Recommendation: On the front end, one way to tackle this is by developing a culture of gratitude within the DAO. While a simple “thank you” may not constitute direct compensation, it can do wonders for perceptions of interaction fairness.
On the back end, activities like having an appeals process after the allocation period and before payments are disbursed can also improve perceptions. This provides a space for community members who felt like they weren’t fairly compensated to voice their concerns. Regardless of whether or not the appeal gets approved, it is crucial to treat the members who are appealing with respect and empathy. We implemented this at DAO Masters after season 1 to great effect. The modified compensation was unanimously approved in Snapshot. Efforts like these can do wonders to improve contributors’ perceptions of interaction fairness, and ultimately overall satisfaction.
One thing I love about working in the web3 space is that the community seems to have a deeply ingrained belief that DAO member satisfaction is important. Rather than viewing contributors as assets, there is a genuine interest in providing for contributors and ensuring they feel valued.
The DAO API is a framework that can help DAOs continue to capitalize on this new wave of thinking. By focusing on Allocation, Process, and Interaction fairness, DAOs can create a thriving community of satisfied members.
I’ll end on this note. I believe the relationship between Contributor Satisfaction → DAO Performance will be even more pronounced than in traditional organizations. Why? Exactly because of the tension I highlighted at the start of this article: DAOs are easy to leave like a community, but members expect to be compensated as if it were a corporation.
DAO contributors have significantly greater power of choice than their corporate counterparts exactly because DAOs have permeable organizational boundaries like communities. Thus, the power of success (and failure) in a DAO falls even more squarely in attracting and retaining the best members. By utilizing the DAO API and focusing on contributor satisfaction, DAOs can ultimately become the best versions of themselves.