Adoption of Augmented Reality is Dependent on Diffusion Through Enterprise Solutions
By now, we are all familiar with the impact smartphones had on society. They disrupted almost every aspect of our lives and there’s an innovative technology that has the potential to match that level of disruption. It is augmented reality smart glasses. Augmented reality overlays a digital GUI on top of our everyday world. Virtual reality is different such that it completely replaces our real world perspective while augmented reality just enhances it. Several recent reports estimate the market for augmented reality to reach roughly $70 billion USD by 2024 and the U.S.’s largest tech companies are all racing to bring a successful augmented reality product to the consumer market (remember Google glass?). Up until recently, the majority of focus has been on gaming and market penetration has been relatively low. I will look at why enterprise solutions combined with an increase of ‘working-from-home’ labor could bring much higher adoption through the perspective of diffusion through different clusters of networks.
First I’ll provide a supplementary explanation on why a focus on gamers leads to slow (or no) adoption. Gamers generally form tight knit groups, or clusters, based on their personal friend network and also which game they’re playing. Let’s assume in this network, each node is a gamer, the bridges represent a relationship between gamers, and the threshold of adopting augmented reality (A/R) is 1/2. A/R provides a better experience, but the cost is a major barrier. So if at least 1/2 of a gamer’s friends use A/R that person will adopt and we’ll call that threshold “q”. In this situation, if node A is the first to adopt, the proportion of node B’s friends (or “p”) is 1/2. p(1/2) >= q(1/2) therefore, B will adopt as well. This will continue throughout the whole cluster until nodes Y and Z. In this case, there will be no penetration in the clusters with Z or Y because their proportion of friends who aren’t using A/R (1-p) is greater than q. In other words, the density of Y and Z’s cluster of friends is too high relative to q. Perhaps they are personal friends of node X but don’t play the same game.
Now let’s consider different enterprise applications of A/R. There are some companies that retooled A/R glasses away from gaming and into telemedicine, manufacturing, maintenance and education. Given recent COVID-19 event’s there is a much greater need for remote capabilities as well as cost benefits that, in reality, decrease the thresholds (q) for adopting. More broad applications of A/R gives a more diverse set of people exposure to the technology, thereby increasing the proportion of people (p) who, by virtue of their job, adopted A/R. We can visualize this first step in the new network with more diverse and distributed first adopters.
Despite the same threshold (q = 1/2), we still see a more widespread adoption because each cluster has a decreased density due to a higher proportion of A/R users in each cluster. Within 5 steps, two clusters completely adopted and everyone will have adopted by step 7.
This is a very generalized representation of the market for innovative technology, but can be used as a tool to supplement adoption behavior. There are several forces in play when deciding when to adopt but hopefully this concept helps explain why rebranding an innovative technology towards a more broad market would help overall diffusion into the population.
https://www.statista.com/statistics/591181/global-augmented-virtual-reality-market-size/
https://www.360marketupdates.com/global-augmented-reality-market-12884346
https://learn.g2.com/history-of-augmented-reality
https://www.vuzix.com/solutions