Diffusion theory to guide adoption of immersive Web3D environments in learning

Through a range of coincidences I’ve been lucky enough to be involved with a team, lead by Penny De-Byl, that was successful in obtaining a Carrick grant to look at how to enable academics adopt the use of immersive 3D environments in their teaching. The project is only just starting, we had our first project meeting just over a week ago at the University of Southern Queensland (USQ), and are still feeling our way as a group and a project.

The purpose behind this post is to think about how CDDU might achieve the rather difficult task of getting a reasonable number of Central Queensland University (CQU) academics interested and enabled to use these environments. In particular because CQU, like many other Australian institutions, exist in a context which makes this somewhat difficult task a great deal more “interesting”.

Demonstrating a cognitive bias, i.e. going back to what I already know, and a typical academic flaw (self-referencing), I’ll fall back on a quick paper which some colleagues and I wrote a few years ago. In Jones, Jamieson and Clark (2003) – does this use of “formal” academic referencing make sense in a blog post? – we proposed the use of a model from Rogers’ (???) diffusion theory as a means for understanding the likelihood and the amount of work required to get a percentage of acacdemic staff to adopt a web-based innovation for learning and teaching. Rogers model is very general, so it isn’t limited to e-learning, but that’s what we were interested and, more importantly, what the track of the conference in a nice part of the world was interested in.

So, in the following I’m attempting to apply the model we adopted from Rogers and applying it to the problem facing the Web3D project. For me, the model is a useful tool to identify the potential factors and give some insight into the problems we might face.

Actually, in the paper (Jones, Jamieson and Clark, 2003), we claimed/thought the following.

We suggest that by examining each of these six components a faculty member can:

  1. Generate a range of issues to consider before implementation.
  2. Predict the amount of effort required to achieve the required rate of adoption.
  3. Predict the level of reinvention.

It is important to note that this evaluation is not based not on supposedly objective benefits of the WBE innovation. Instead this evaluation will be based on subjective, contextual, and environmental issues that are unique to each situation.

Variables which influence the rate of adoption

The version of the model we adopted from Rogers looks like this.

Diffusion theory model for rate of adoption

Perceived innovation attributes

When someone is introduced to a particular innovation (e.g. immersive 3D environments) they perceive that innovation to have some characteristics, some attributes. Rogers (1995) work looking at a huge array of innovation diffusion projects hasidentified a set of five innovation attributes that most strongly influence whether or not someone will adopt an innovation. Those five attributes are:

  1. Relative advantage – The degree to which an innovation is perceived as better than the idea it supersedes.
  2. Compatibility – The degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters.
  3. Complexity – The degree to which an innovation is perceived as difficult to understand and use.
  4. Trialability – The degree to which an innovation may be experimented with on a limited basis.
  5. Observability – The degree to which the results of an innovation are visible to others.

My believe about how the Alive3D technology and its application to learning and teaching at CQU, will be perceived at CQU goes something like this

  • Low relative adantage
    In the CQU context innovations, particularly fairly different ones like this, are not going to be perceived as offering advantage to teaching staff.
  • Low compatibility

    For the majority of folk e-learning is an LMS, sharing documents, holding discussions and similar. Few have done anything within a 3D environment, even fewer have created 3D environments. For the small number of folk who use Macs, the fact that the software only runs on Windows, will further reduce compatibility.

  • Fairly high complexity
    They haven’t done 3D. E-learning and technology, for many, most?, is perceived to be hard. 3D will be perceived to be harder.

Triability and observability will both be fairly low. This will be something that we can build on and improve.

Please recognise that there will be never anything like a consensus view amongst academic staff, this is meant to be very broad brush guessing exercise on my part. It is also not meant to represent any objective set of characteristics of the technology, it is meant to represent how academics at CQU will apply the bounded rationality and the cognitive biases which we all have to how they perceive this project and its technology the first time they see and hear about it.

Innovation decision

When deciding whether or not to adopt an innovation Rogers (1995) identifies three types of decisions

  1. Optional – Each individual in a social system to adopt or reject the innovation.
  2. Collective – The social system makes a consensus-based decision to adopt or reject an innovation
  3. Authority – Made by those in authority with the expectation that the social system will follow that decision.

For most academics, the adoption of Web3D will be an optional decision. For those working within more constrained programs/disciplines it might require either a collective (consensus from the whole disipcline group) or an authority decision (head of program/school).

Rogers indicates that authority decisions generally show the fastest rate of adoption and that optional decisions are made more rapidly than collective decisions. He also suggests that some types of authority decision can also suffer from large amounts of “reinvention” – the degree to which an adopter modifies the innovation in the process of its adoption and implementation.

Communication channels

The Rogers view of diffusion of innovation is of a particular type of communication which is aimed at reducing uncertainty about the innovation. That communication occurs through a channel, a particular medium. The nature of the channel, its attributes, are suggested to influence how effectively the communication is. Diffusion theory characterises communication channels based on the following spectrums

  • mass media or interpersonal; and
    Mass media channels, which enable small group to reach a large audience, are a rapid and efficient means by which to inform an audience of an innovation and lead to changes in weakly held attitudes. Interpersonal channels, links between two or more individuals are more effective in dealing with resistance or apathy.
  • local or cosmopolite.
    Cosmopolite communication channels originate from outside the social system. Potential adopters of an innovation rely more on subjective evaluations from other individuals like themselves who have previously adopted the innovation than objective evaluations of an innovation.

Social system

Every organisation is a collection of social systems. The various connections/networks of individuals that have some thing in common. Typically the joint problem-solving required to achieve common goals. For example, teaching within the same program or perhaps at the same institution. Diffusion theory identifies the following characteristics of a social system which influence adoption

  • Social structure – the formal arrangement of units within the social system.
  • Communication structure – the informal, interpersonal networks which link the social systems members.
  • System norms – the established behavior patterns and beliefs that are common amongst the members of the social system.

The use of Web3D is not likely to challenge the system norms, at least not to a great extent. If might be seen as just another learning innovation for which the responsibility for evaluating, selecting and using is left up to the individual academic. This might change somewhat in some component social systems.

Lessons or activities for Web3D

The following are a collection of “action items” which might apply or which might need to be done within the context of the project

  1. Do some evaluation of academic staff’s perception of Web3D when they are initially (or prior) to hearing about it in order to test whether or not some of the “beliefs” above bare out.
  2. Design the communication strategy (e.g. initial presentations, websites etc) to maximise the chance people while perceive the innovation in a way likely to encouage adoption.
    • Be able to demonstrate in a clear way the advantage this approach brings to the specific context.
    • Frame the communication about the innovation so that it is “fits” with current contextual practice.
    • Provide simple and straight-forward ways through which people can play with the technology – perhaps straight after the introduction.
    • Ensure that what work is done is observable to others
  3. Should theoretically need to know more about the particular program/discipline groups and, in particular, about how decisions of this type are likely to be made.
  4. Aiming to implement Web3D as a simple trial, that doesn’t require consensus or perhaps authority decisions might also be worthwhile.
  5. Identifying a decision-maker with interest in Web3D might also be useful.
  6. Initially, awareness will be raised via a mass-media, cosmopolite communication channel – a CQU wide presentation. It needs to be followed up with a range of interpersonal and local discussions.

Problems with all this

Diffusion theory can contribute towards “development” approaches that are more focused on the human, social and interpresonal aspects of diffusing an innovation rather than on the purely technical aspects of the innovation.

But, as with any theory, technology or idea, diffusion theory is not perfect. It brings with it, it’s own set of blinders. It tends to have an assumption that the social system/the adopters need the innovation. There is a pro-innovation bias. In this case, the project team has made a decision that Web3D is good and that it will make a difference to the practice of learning and teaching. We’ve made the choice and, at least, I am attempting to use diffusion theory to convince (perhaps brainwash) people to adopt Web3D. It can lead to the situation where those who adopt Web3D are seen as superior to the recalcitrants who have not made the positive adoption decision.

To some aspect this is true. I am part of the project team, part of my “success” will be measured by how widely and well Web3D is adopted and used within CQU. I am, not suprisingly, attempting to maximise this. However, I do need to ensure that this process is implemented and perceived to be providing the information about the innovation so that academics can make informed decisions based on their perceptions and understanding of their own context.

I need to be honest that this is not a silver bullet. Which is good, because it isn’t.


David Jones, Kieren Jamieson, Damien Clark. (2003). A model for evaluating potential Web-based education innovations. Paper presented at the 36th Annual Hawaii International Conference on System Sciences, Hawaii.

Rogers, E., (1995), Diffusion of Innovations (4th Edition), New York: The Free Press.

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