When people in authority want the rest of us to behave, it matters – first and foremost – how they behave.
This is called the “principle of legitimacy,” and legitimacy is based on three things: First of all, the people who are asked to obey authority have to feel like they have a voice – that if they speak up, they will be heard. Second, the law has to be predictable. There has to be a reasonable expectation that the rules tomorrow are going to be roughly the same as the rules today. And third, the authority has to be fair. It can’t treat one group differently from another.
--from Malcolm Gladwell’s book David and Goliath
The vast majority of us are plugged into the mainstream social networking platforms, such as LinkedIn, Facebook, Twitter, and whatever else the hipsters are using to organize their beard-comparison meetups at the local Starbucks (yes @bniaulin, I mean you). And as more and more of these tools go mainstream, have you noticed that the quality of what is being shared – at least within the public domain – is dropping dramatically? Some would argue that the quality was never there….but we’ll just have to agree to disagree on that point. As I was reading Gladwell’s book, my mind was on the very difficult task of measuring productivity, which I just wrote about over on the Beezy blog, and how quality and authenticity are key attributes of productivity. But something struck me about this “principle of legitimacy” and whether different collaboration methods have real or perceived differences in legitimacy, which impact how we view authenticity, and, therefore, productivity.
While there have been studies and books written on the science behind social networking for more than 50 years, with the leaps and bounds we’ve made around the technology behind modern network science, we are barely skimming the surface of what we can do with this data (one of my favorite books is Linked: How Everything is Connected to Everything Else and What It Means for Business, Science, and Everyday Life by Albert-Laszlo Barabasi). Most of us are familiar with just the top layer of our networks – with content and conversations from the people with whom we are directly connected. And through some tools, we can see into our second level (and sometimes third level) relationships. But how do you know whom to connect with to strengthen your overall network? What content is hidden below that top layer which may be a better match for what we’re looking for? Are you tracking the quantitative measurements of the content you have access to and which may be the best match/fit for your business needs, or are you relying on the bare minimum – which usually means its above the fold on your occasional visit to the social platform of your choice, i.e. if you have to dig, you’ll never find it? And the further you get away from knowing the source of the content, does that negatively impact our view of the legitimacy of that content?
On the topic of quality and authenticity, do you base your selections on what is most shared by your peers – even when they, too, are just skimming the surface of the shallow end of the content and context pool? In which case, does more people on that network – most of which are skimming along, not accessing or sharing quality content – diluting the quality of your network, and driving down productivity? You see how I am pushing my own bias on the legitimacy of the content created within the 2nd or 3rd degree of my network?
What I have been thinking about is the inverse quality of internal social networks versus external. What is the difference between them? Community management and corporate standards play a large role, I think. But there is also the importance of having a shared sense of purpose. If I can beat up on Facebook for a moment – even if you launch a public network with the best of intentions, allowing anyone to invite and join, the quality of conversation will immediately degrade because there is no shared sense of purpose, no organized commitment to quality. Contrast that with a socially-active internal group, sharing internal and external content and links and ideas, having conversations, inviting others with that same sense of purpose and shared standards. With authenticity of intent and purpose comes improved quality, and, arguably, improved productivity.
I suppose that what I am describing is really the vision of what machine learning can provide – which relies upon and taps into our social networks. With technologies like the Office Graph and Delve, we begin to see corporate systems that understand and interpret our profiles, that suggest connections outside of your existing networks -- based on explicit network queries that we make manually, on our networks and social activities, as well as automated, machine-based connections that are based on what the system interprets from our activities and the wisdom of the entire community. Clearly, we need to get smarter about the tools we use and the types of analysis we conduct if we hope to get the most value out of social networking. With the combination of social collaboration and machine-learning, we achieve the first and second points that Gladwell makes: we have a voice (social) and predictability (machine-learning), but fairness is a tough one.
I’m going to continue to stew on that one, and will write on this topic again in the future.