Mistaking the Edges for the Norm

One of the best lessons from social quantitative analysis in grad school (public policy) was learning to understand if you are viewing edge cases or the norm (mainstream). Humans have some common traits, but when you start to design or develop any sort of program (be it government services or social software) you start to realize that social at scale has many variations to how humans are social.

When taking that deep understanding we must understand if the trends we are seeing are the edges (or even outliers) or the norm. The common elements that cause the variation (often very large variations) are often driven by culture (as well as sub-cultures) and personality types.

Many of us who were early to blogging and many other social platforms were very much outliers and at or beyond the edges. We built and designed tools and services based on our personality types and traits. When you have 1.5 billion people the internet getting 70 million or even 200 million people that are similar to the edge case traits can be somewhat easy. What is really difficult is that next 90 percent. Keep in mind people use social tools very differently. What has worked for the very early innovators through early adopters is extremely different from the different personality types that will follow.

This gap in understanding that the world is not like us has not become real to many building social tools. But, to some it has hit hard, very hard. Much of the early Web 2.0 theories about social web patterns were looking at the edges and mistaking them for the norm. This was relatively easy to see if you have a background in social analytics and adoption trending through a society at scale.

To get beyond the edges you have to go deep, very much like danah boyd has done with her work. The work danah has done is deeply helpful as it surfaces the difference in understanding across personality types, age ranges, and many cultural influences. She deeply understood the problem that most people on line (youth and adults) were not openly social as was (and sadly still is) the common assumptions of things to come. Privacy and small groups is much more common. Today we see Facebook privacy setting with 70% or more with “Friends Only” or tighter for sharing information ([Pew’s Privacy management on social media sites” report).

Gamification

This understanding the edges and norms differences is also incredibly helpful for things like gamification, which can cause really nice upticks in usage of social services with the innovator and early adopter types (in the Technology Adoption Lifecycle, that is the core of Geoffrey Moore’s Crossing the Chasm framing). But, for the rest of the users it is either non-influencial or is deeply problematic. The mix of benefit and loss is essential to understand. At IA Summit I had quite a few discussions with UX people trying to fix the communities that were damaged by gamification in the long run after a nice initial uptick. It is a tough problem and a real issue to grapple with. This is incredibly noticeable on inside the firewall communities as there is a fixed user base and you can easily see who participated and how over time and the shifts (well, you do need access to the data, which some vendors don’t provide access to).

Today many of the one year to four year old social software deployments in organizations have gone through the edge types and been finding gaps in their services and tools offered as they work to get to the norm types.

The tools must change and adapt to the edges and the norms and the two user sets don’t really work in the same way. We have a lot of seeing, thinking, understanding, and building a better path for the mainstream folks as we bring people along on this fantastic transformation those of us on the edges have been through the last 20 years and more.

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One response to “Mistaking the Edges for the Norm”

  1. thomas@vanderwal.net Avatar

    I posted the following response to Gamifying work. Seriously? by Adrian Chan.

    I have posted a piece that starts to get to answer of this in my Mistaking the Edges for the Norm. The front side of gamification causes a decent uptick, but the backend of that same curve can be not so advantageous.

    Gamification builds to a default set of assumptions about gaming, but game developers know there are many game player archetypes that don’t overlap nor intersect. What works for one archetype may completely alienate others, which is what is happening in most services. Keep in mind game players opt in to the game genre and archetypes they enjoy and not everybody is a fan of games. Some personality types fall into nice mapping of game focussed uses to roles and interactions, but it doesn’t work for the whole. Making the mis-assumptions has problems for the whole in many, if not most, cases where systems have had elements of gamification implemented.

    What is missing is longitudinal data measuring the whole before and after. The fallout and problem areas arise with gamified systems after 3 to 6 months it seems with some parts of the community that were active, no longer feeling comfortable or like the system is for them. People who have been highly valued contributors may no longer contribute and often point to the gamification as what alienated them.

    The place to look in organizations that have been running systems is often the UX folks or others whom are tasked with fixing the problems. Once you find them and ask about issues and disruptions in the use (positive and negative) the stories and concerns arise. Rarely do the managers of the services discuss the problems (some protecting their own interests and consider this just something to work through or other needed fix). I’m really hoping somebody does serious research on this as there is a real trend and knowing it and documenting it will really help the whole.

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