Fairness Heuristic Theory & Networks

In my last post, I discussed Soft Systems Methodology, which I am using to explore the role of fairness in collaborative networks. In this post, I share the basic premise of Fairness Heuristic Theory. In my next post, I will talk about what I have been learning about how network leaders view their network culture.

Fairness Heuristic Theory

E. Allan Lind and his colleagues first proposed the Fairness Heuristic Theory (FHT) in 1992. In brief, the theory is that perceptions of fairness act as a "rule of thumb" for measuring whether one should invest in a collaborative effort. If one perceives that fairness will reign within that collaboration, one is vastly more likely to to lend one's resources and--more importantly--one's identity to the effort, according to this theory.

According to Lind, the most essential aspects of people's perception of fairness are rooted in whether one feels valued and has standing as part of the group. They render this judgment based on feelings of inclusion, ability to effectively raise concerns, and sensing respect in the way others treat them. In cases where the person feels they have standing and will be treated fairly, they will willingly become part of a whole.

Fairness Heuristic Theory in Collaborative Networks

FHT comes out of organizational science, and particularly explores how employees might become willing collaborators in the plans of their boss. Why, for example, should someone put in long hours at work rather than please themselves with more spare time? Why should they do a job very well rather than do what is easiest for them personally? Fair treatment (including a sense of standing in the group), Lind and others argue, is a significant part of the answer.

But the tension between the part and the whole is, I argue, at the heart of collaborative networks as well. A network is not a top-down structure giving marching orders to soldiers, it is a bottom-up gathering of diverse perspectives on a complex problem. Members must autonomously agree to collaborate to achieve a purpose, even while their own individual purpose is distinct.

Consider the example below, in which a network has formed to end malnutrition. Member A experiences the part/whole tension in that this organization (which focuses on childhood malnutrition) must be willing to lend its identity and resources to some efforts that are beyond the scope of its own concern: for example ending malnutrition among the geriatric population. Members B and C experience the part/whole tension differently, in that their mission encompasses many things beyond the scope of the network. In this sense, the network competes with other valuable work they might do.  In all three cases, there may be times that the good of the whole network actually runs counter to the member purpose: for example perhaps in some cases, increasing arable land decreases rather than increases hunger--putting Member B in a dilemma.

Now, when these members are asked to participate in the network, they are confronted first not with an outcome, but with the question of participating--that is, collaborating. They must decide whether, in essence, to write a check that consists of their identity and resources for a product (the end of malnutrition) that is a very long way from delivery. They could choose not to participate, or, as those of us in collaborative networks know very well, they might just to "participate" just enough to stay on the mailing list so they stay in the know, but without truly lending their identity and resources to the network's purpose. But they just might choose to participate at a very high level, which, as it happens, is the only way they can achieve the shared purpose.

Unlike an organizational setting, there is not a strong authority figure who can compel at least some level of participation. Members must decide to do it entirely themselves, making fairness even more essential.

The point of my research is to test perceptions of fairness among network stakeholders and to develop feasible recommendations to improve perceptions of fairness. Ideally, subsequent research would revisit both the desirability and feasibility of implementing them, and determine if resultant improved perceptions of fairness do indeed improve member willingness to collaborate deeply.

I will return to Fairness Heuristic Theory later, but my next post will share what I am learning already from network leaders about their network culture. This is important because, without understanding the landscape, it would be very challenging to make recommendations that are both relevant and feasible.  

Using Soft Systems Methodology to Explore Networks

Welcome to my blog on systems thinking in collaborative networks. I am interested in a wide range of systems thinking as it relates to network practice, and my current research focuses specifically on the role of fairness in collaboration. This research is being conducted to fulfill the requirements of Masters in Science from the Open University's Systems Thinking in Practice program. The OU is based in the UK, but I am in the US.

In this post, I share my choice of methodology, Soft Systems Methodology, and why I believe it is suited to collaborative networks. In my next post, I provide an overview of Fairness Heuristic Theory and its potential application to networks.

Soft Systems Methodology (SSM)

Soft Systems Methodology (SSM) was developed in the 1980s by Peter Checkland. It has evolved  into a modern form with four stages: 1) finding out about a problematic situation, 2) formulating purposeful activity models, 3) using the models for debate and reaching accommodation among stakeholders, and 4) taking action to improve the situation.  However,  these steps do not necessarily happen in the order listed, or only one time. An SSM intervention is iterative.

Source: Giles A. Hindle in Case Article—Teaching Soft Systems Methodology and a Blueprint for a Module

Why "soft" systems? A key insight Checkland contributed with this methodology was that truly complex situations are not "real" or "hard" in the sense that they can be objectively defined or engineered. A complex system is not simply an extremely complicated technical problem, but is confounding in that it's defined differently, depending on your point of view. The boundary of the system, the output of the system, and the dynamics are dependent on perspective. The role of perspective is bound inextricably with complexity. 

For example, if you point to a truck and ask different people with different points of view the main thing that truck produces,  you might receive any of the following answers: transportation, ability to haul, pollution, prestige, amelioration of a mid-life crisis, or a set of bills. Imagine the truck is jointly owned by people who hold all these perspectives. If the truck stops working, different people will have very different reactions to this, and understanding the complicated mechanics of the truck will only get you so far in being able to improve the problem.

I like Hindle's diagram (above) because it puts the role of multiple perspectives at the center of the SSM intervention. A key part of finding out about the problem is enabling diverse stakeholders to define how they see the situation and the context it sits within. One technique for this is drawing pictures that represent what is happening in the system: the structures, the processes, the relationships. Participants also consider the roles, norms, and values at work in the situation.

Once one has made a good start on finding out about the situation, the group engages in thought experiments to make models of activities that are relevant to the situation, and they use these as a basis for coming to an agreement on how to improve things. These discussions are based not only in what would be desirable, but also what is feasible, considering the relationships, roles, norms, and values already present in the situation.

The final stage of the methodology is taking action, which results in a new and (hopefully) improved, but (likely) still problematic situation, which needs to be explored. This may sound like embracing Sisypheanism, but it's quite different. It's simply recognizing that in complex situations, today's problems are often the result of yesterday's solutions. Hopefully today's problems aren't as bad as yesterday's problems were, because hopefully we have intervened wisely. But if a single set of actions could solve the problem, it probably wasn't so complex to begin with. In SSM, we have a tool for managing complexity, rather than for being a superhero who solves everyone's problem.

SSM and Collaborative Networks

I draw from many different fields of Systems Thinking in my work with Networks, but I find  SSM particularly well-suited to networks because SSM embraces the role of multiple perspectives throughout the entire process. 

SSM is also focused as much on learning as it is on outcomes. Collaborative networks are still emerging as a way of impacting complex situations, and we have so much to learn! By using a methodology that is grounded in learning, we reap both the benefits of improvements we undertake, and we also learn how to learn--collaboratively. 

In my next post, I will talk about Fairness Heuristic Theory and why I am using SSM to explore its application to collaborative networks.