Which got me thinking about what I was up to 10 years ago.
At the time I was a junior PhD researcher at the University of Plymouth. Bizarrely my old homepage is still live, though mercifully I’d updated it in 1999 with fewer flashing GIF’s and roll-over image maps, though the obligatory animated email gif is still there (the email isn’t live however).
A large part of my research (1997-2000) was looking at systems theory as it applied to ‘human activity systems’, so I’m going to cheat (slightly) and reference some work that wasn’t directly about Social Media (or even web-based technologies). Though I was based in the School of Computing, my background is in engineering and the research group was mostly engineers, economists and psychologists. We were interested in how systems theory could be applied to particular social groups (mostly engineering companies in this case) and in particular the processes that those social groups used to achieve certain aims (generally converting some specification into a manufactured product). However, I believe there is good reason to think that much of that research can be applied to social media application in other business endeavours.
Prof Peter Checkland (Lancaster University) is a Chemist that worked in industry on complex engineering problems and eventually moved to the most complex systems of all, those involving humans (unfortunately his seminal work is a book, 1981, not an online article).
Systems are generally recognised by some fundamental principles;
- Boundaries – there’s a bunch of stuff that’s “inside” the system, and a bunch of stuff that’s “outside” the system (there will usually be an argument over where to draw the boundary but that almost defines the fact that you’ve got a system)
- Inputs and outputs – stuff crosses the boundary, this can be physical or non-physical [you can have a ‘closed’ system but they’re generally rather boring and hypothetical]
- There’s some transformation, i.e. difference, between the inputs and outputs
- There are components within the system; a single component is not a system
- Systems are nested; within large systems are smaller systems
and most importantly
- Emergent Properties – you can’t describe the performance of the system just by analysing the component parts
Checkland is important because he was one of the first people to try and describe the messy company and organisational situations he was working within from a systems perspective (building on much of Bertalanffy‘s work between 1934 & 1969) . He identified boundaries and within those boundaries the sub-groups that actually made the company work. He identified information and flows of power within the organisation, and across those boundaries. He was able to sketch out the ‘actual’ human activity system, rather than the business or computer information system. It was the systems characteristic of emergent properties that led to them not performing as planned, and gave rise to the law of unintended consequences (previously identified in social sciences by Robert Merton (1936) but not explained).
But what does this mean for the social media strategist?
Well it means that, despite our shiny shiny toys, there is quite a bit of good research and clear thinking about how people work in groups and in particular how we can design such systems. No matter how carefully we design them, there will be emergent properties; but that doesn’t mean we shouldn’t design them in the first place.
There will always be boundaries, some stuff will be “inside”, some stuff will be “outside”. You don’t want everything to be “inside“; even planet earth isn’t a closed system.
Stuff needs to cross your system boundary, and it needs to be transformed en-route to becoming an output. I put my details into Facebook (as do 350m others) and a whole bunch of RSS feeds, and I get a ‘useful’ homepage about my ‘friends’. I can put photos, comments, stories, whatever in, and people can further transform them with additional comments, links to other people, etc. True, you can’t automagically get that data out of Facebook, but you can log in and read what’s there. And reading what’s there is one form of taking information out of the Facebook system, as are social networks, etc.
Of course there are lots of component software chunks and sub-systems within Facebook. Each casual game on Facebook is its own system, nested within Facebook. Each fan community is a nested system. Each discussion board is, potentially, a sub-system, nested within a fan page, nested within Facebook, nested within the Internet, etc. Depending on where you draw the boundary, everyone that’s on Facebook is also part of the system…
And of course there’s a ton of emergent behaviour that wasn’t predicted (or predicable).
So what can we do about/with it?
The first thing to note is that, from my experience, most people aren’t very good at meta-cognitive thinking about systems theory. That is, they are used to living within systems (social, organisational, leisure) but they don’t actually spend a lot of time thinking about those systems, and even less thinking about how they are thinking about them.
This means that people will usually try to apply existing social behaviours and norms to on line systems, and if that doesn’t work they get frustrated/angry/disillusioned/etc. You either build your on-line social media system exactly like ‘real’ world (but then why would anyone be interested in your system?) or educate people into the operating of the new system. That’s why all games have tutorial / training built in.
In order to develop a training programme you need to understand, and be able to communicate, the designed purpose and functioning of your system. One way to do this is draw a picture of it, not a UML diagram or a wireframe, but a human activity system diagram. You don’t need to use any ‘standardised’ modelling nomenclature, so long as you and your team understand it and it covers the basics above.
|Michael, Rick Chapman, and I spent some time recently thinking about modelling social media systems. We tried to cover the basics, without employing a formal systems modelling methodology. Its not perfect but I think it’s a good start.
You should know where the boundaries are, what the expected information flows are going to be, the transformations and components that will do the transforming, and what the wider emergent property will be. That will all change once the system begins to operate but at least you’ll have a blueprint and can either take action to bring the system back into the original concept or decide to take things in a different direction.
We did quite a lot of this within my old research group, and its surprising how good a consensus you can arrive at for generic systems diagrams.
This early draft is far from ‘perfect’ but I think there is something of value if you’re building, or thinking about social media, to have a model similar to this in your toolbox to refer to.
Conclusion & Caveat
In conclusion, some thinking time about the network you’re trying to build is valuable. The tools you employ should come afterwards; twitter is not a social media strategy. There are lots of good, well established frameworks to think about social networks, systems of activity, etc. You don’t need to follow slavishly the minutia of their particular quirks and peculiarities but you should understand why they are there and why you are ignoring them.
The caveat: feedback loops are a feature of systems. The huge difference that digital technologies have brought is the near frictionless feedback loop. There is almost no transactional cost to publishing a comment and for that comment to then be republished to +350m people (it happens both automatically via services like posterous and twitter-bots, and through the retweet/comment feature in all social media services), and re-re-published endlessly. That is something that we haven’t modelled effectively yet. The good social media marketeers amongst us know how to achieve this, even if they don’t fully understand the why.