Reputation

A LinkedIn group I subscribe to started a discussion this weekend on recommendations: what is a good number to have, whether or when you’d hide a recommendation. This got me thinking in a broader sense about reputation: how do you establish and protect it, and how do different online communities go about it?

To start with LinkedIn, it has two main ways of establishing reputation: the aforementioned recommendations and the connections. Here it is very much “it is not what you know but who you know.” Status is derived from the number of connections one has and with whom one is connected. Behind both connections and recommendations lies the question of how many to have and how much is too much. For myself I like to able to say something comprehensible about a connection when asked so if I’ve had mainly some casual interaction with a requester of a connection then I am likely not to accept the invite. I know that others see LinkedIn connections much more similar to the exchange of business cards and thus apply a much lower bar. With recommendations I believe a strict management is appropriate. The value of a LinkedIn profile diminishes with an increasingly large number of recommendations. Aiming to have a few recommendations from select persons one has worked with communicates much more value to a profile.

The same principle applies regarding the recommendations you have written for others. A profile that shows a large number of recommended people then I do start to wonder about the objective judgment of that person: seemingly the bar is quite low. Especially since recommendations are typically written in rather glowing language.

An additional question in this weekend’s discussion was how much value one gives to recommendations when evaluating a person for example in the course of a job application. Here a number of aspects start to play. If I don’t know the persons who wrote the recommendations then it becomes harder to judge them. When looking at recommendations from the perspective of reputation then the main shortcoming surfaces: it is a one-way system, meaning that a person will – understandably – only display positive recommendations.

Naymz (http://naymz.com) tries to go about this in a different way. In addition to its equivalent of recommendations it introduces RepScore and Reputation Network. RepScore is a points system whereby you get points based on the reputation of those in your network and their opinion of you. You also get points for how complete (in Naymz’s opinion) your profile is which seems somewhat self serving. To endorse someone on Naymz you answer a few yes/no questions and based on these the person gets reputation points. In addition you can submit a written endorsement ala LinkedIn’s recommendations. And you can click to be listed as a reference for someone. This is a much richer means of establishing reputation than LinkedIn. Similar to LinkedIn the recipient of the endorsement has the choice to approve and display it and thus, I assume, whether or not it contributes to the RepScore. I would like to be able to drill down on someone’s RepScore and see how it was built up. Certainly interesting is that the reputations of those in your network influence your reputation score: a quantification of “it is who you know”.

In the case of Twitter there’s an ongoing debate if the value is in the people you follow or in the people that follow you. There definitely is a celebrity angle: some have thousands of followers by virtue of their public position rather than necessarily being that interesting or having such valuable, insightful things to say. And from a reputation perspective Twitter is hampered by the broad presence of spam accounts.

Open source communities like the Apache Software Foundation (http://apache.org) like to describe themselves as meritocracies. One rises up in the community by virtue of the contributions made and their quality. In return one gets more exposure, can gain more responsibilities (e.g. committer status, project lead, get elected to a board) and thus increase authority. And many of these communities appear to be quite good at managing this in a flexible, fluid manner without a rigid set of rules. For some communities, typically those led or managed a corporation this is more difficult; certainly initially when most of the participation comes from inside the company and specific, deliberate actions are needed to let non-employees gain status.

The advantage – regarding the meaning of reputation – that open source communities have over general purpose networks like LinkedIn is that reputation is determined by people doing work together. And so reputation within these communities is deemed very important with members becoming very protective over how they perceived by their peers which on occasion results in public debates. This is because here reputation is implemented in a more complete sense: there’s not only the notion of recommendations and endorsements but also criticism and opposing views.

Professional networking sites like LinkedIn, Plaxo, Naymz and others are all relatively young. I like to think about how functions like endorsements and connections play out over time. There does not yet appear to be an elegant way to let endorsements, connections and references age. How valuable is an endorsement 15 years from now to you and the readers of your profile? Even when you are conservative in accepting connections, over the course of your career you’ll still collect a large number. I would like to have newer connections stay near the surface and let older ones drift down. Nonetheless, LinkedIn Profiles and their competitive counterparts are becoming the professional resumes of our time: a living document that tracks our professional progress. To that extent, it assumes me when hiring managers still ask for the printed, static resume.

How do you navigate the nebulous and fluid world of assigning reputation?