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The year is 2005 and the Internet has just recovered from the first dotcom crash. Geoffrey Moore publishes “Inside the Tornado” in an attempt to explain the hypergrowth phenomenon and introduces the “Technology Adoption Lifecycle” model. The book immediately became an investors bible to predicting when to invest in hypergrowth stocks.
Over the next few years, Moore’s model proves effective in determining new tech growth but fails in predicting which companies will survive and which will fail. In 2014, he publishes “Crossing the Chasm” as his answer to the problem. In it, he delineates a point in the adoption lifecyle where companies either fall off the cliff transitioning early adopter strategies to new mass audiences.
While “Crossing the Chasm” proved not to be an investor’s secret weapon due to the complexity of stock market behavior, it is actually a very effective Change Management planning tool for gaining acceptance and promoting adoption of new processes, tech tools and HRIS initiatives. Glasscock & Associates consultant, Mark Abouzeid, has developed a whitepaper (link) that uses Moore’s TALC profiles to map adoption types to motivation and engagement priorities.
“New technology presents risks for many employees. They react differently toward this risk based on their innate nature, their role, company power dynamics, and the behaviour of other employees. The Technology Adoption Life Cycle (TALC) models how different groups of customers adopt to discontinuous innovation at different times. This model helps policy makers and HR change managers to build adaptive strategies to gain acceptance acroos the organization.”
Five stages of the adoption process
Most new technologies, including disruptive innovation, follow a similar technology maturity lifecycle, which while similar to a product life cycle, applies to an entire technology or a generation of technology.
From a layman’s perspective, the technological maturity or TALC phases can be broken down into five distinct stages.
1.Bleeding edge – any technology that shows high potential but hasn’t demonstrated its value or settled down into any kind of consensus. Early adopters may win big, or may be stuck with a white elephant.
2.Leading edge – a technology that has proven itself in the marketplace but is still new enough that it may be difficult to find knowledgeable personnel to implement or support it.
3.State of the art – when everyone agrees that a particular technology is the right solution.
4.Dated – still useful, still sometimes implemented, but a replacement leading edge technology is readily available.5.Obsolete – has been superseded by state-of-the-art technology, maintained but no longer implemented by the specific firm.(Moore)
Moreover, the primary phases of the Technology Adoption Life Cycle can be mapped to stages of the product life cycle providing specific insight into market dynamics at any stage of technology adoption defining target user profiles, product/ service requirements, communication channels, support dynamics and stakeholder strategies.
Five factors that determine adoption rate
The rate of adoption; the relative speed with which members of a social system adopt an innovation; is usually measured by the length of time required for a certain percentage of the total potential population of users to adopt an innovation (Rogers 1962). The rates of adoption for innovations are determined by an individual’s adopter category. In general individuals who first adopt an innovation require a shorter adoption period (adoption process) than late adopters. There are five specific factors that help determine the adoption rate and used properly can minimise the time required to pass from one phase of adoption to another.
1.Relative Advantage: how improved an innovation is over the previous dominant design.
2.Compatibility: What is the level of compatibility that an innovation has to be assimilated into a hardware/workflow and networks?
3.Complexity or Simplicity: If the innovation is difficult to use, requires significant training or implies inherent implementation risk, pragmatists will not likely adopt it.
4.Trialability: How easily can the innovation be experimented/tested/piloted? Does it allow for staged integration as the complete solution is being adopted? If a decision-maker has a hard time using and trying an innovation, adoption is less likely.
5.Observability: Pragmatists and the mass-market decision-makers are led primarily by the actions of competitors and needs of clients. The extent that an innovation is visible to others will drive communication among peers and networks and will in turn create more positive or negative reactions.
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