Method: the VRIP matrix for analysing current and ideal tech adoption

Over the last few days i’ve been involved in yet another one of the long winding discussions that occur in the academic tech business concerning how a particular requirement is best satisfied, and what part institutional IT should play in providing a solution. In this case it is “video and audio streaming live events” (yes, that one again). The debate is messy in two dimensions:

  1. “Live event” in an academic context covers a wide range of quite different things, from lectures through to physical theatre workshops.
  2. There’s a huge range of possible technology elements, including cameras, mics, encoders, streaming platforms, ad-ons to common tools (YouTube, Facebook) with a super complex matrix of features and quality levels.

Fortunately we now have analytical tools that can help us – so long as they are used systematically. And here are some notes I have written to explain it to colleagues.

The VRIP matrix (as I’ve decided to call it so as to sound more impressive) emerged out of work done on digital capabilities, and the visitor/resident model (Dave White, Helen Beetham, Alison Le Cornu, Lawrie Phipps, Donna Lanclos, James Clay and others). This is what a matrix looks like. The idea is that a tool/technique can be placed onto the matrix to indicate the type of adoption and integration into institutional services. This can be used to show the situation as it is or the ideal we think we should design and work towards. We can also indicate the extent of adoption by using a colour coding schema. Typically we map out the situation regarding a specific group (e.g. undergraduates).

The matrix can be used to graph the current situation – e.g. where most people are at with a tool or technique now. It may also be used to show the ideal degree/type of adoption and support.

At the institutional end of the matrix we place things that are or should be chosen, provided and supported by the institution. At the other end, we put personally (or communally) chosen, owned and supported.

On the visitor end of the visitor/resident axis we put tools and techniques that people use less often and which they don’t become familiar (they often have to relearn each time they visit). And at the resident end are the tools and techniques that they live and breathe all the time.

So where is “streaming a lecture” on the matrix, for specific groups of people? That’s an empirical question for which we haven’t really got an answer.

And where should it be? That’s a complex strategy question.

And how do we get to the point at which a tool/technique is in the right place on the matrix for the right people?

So this is the kind of sophisticated analysis, that needs to be backed up by an investigation (of the kind I’m doing for VR at the moment).