Characteristics Affecting rate of adoption level of adoption for new products

Way back in 1962, Everett Rogers, fondly known as the father of the Diffusion of innovations theory, listed five attributes which affect the rate of adoption: relative advantage, compatibility, complexity, trialability and observability. The relative advantage is the degree to which the potential user perceives the innovation to be better than what it’s replacing. The compatibility is the degree to which the innovation fits in with the values, practices and needs of the potential adopter. The complexity is the degree to which the potential adopter perceives the innovation to be difficult to understand and then use. Trialability is the degree to which the innovation may be tested or experimented with before full-scale use. And finally, observability is the degree to which others can easily see the results of the innovation. Rogers contended that innovations that are perceived to have low levels of complexity and high levels of relative advantage, compatibility, trialability and observability, will be more quickly adopted than those that do not.

While various researchers have tinkered around the edges of that theory, the ones we would like to draw to your attention today are actually some Australians who we think have helped take our understanding to the next level. Geoff Kuehne, Rick Llewellyn, David Pannell, Roger Wilkinson, Perry Dolling, Jackie Ouzman, and Mike Ewing in 2017 published the paper, Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy. 

In that paper they discuss the Adoption and Diffusion Outcome Prediction Tool (ADOPT), which was devised to better estimate the likely extent and rate of adoption of new agricultural practices and technologies. We have previously discussed ADOPT, highlighting the way in which it can be used to predict how many farmers might adopt a new practice or innovation. However, in this post we are focusing on what they identified as two overarching factors that were influencing the adoption process: the relative advantage of the practice, and the effectiveness of the process of learning about the practice. 

Their work suggests that relative advantage is the main driver of how many in a population decide to adopt, while the learning process influences the time lag before decisions to adopt are made. The conceptual model is a quadrant with learning and relative advantage across the top, and the population and the innovation down the side. This then results in population-specific influences on the ability to learn about the innovation; relative advantage for the population; learnability characteristics of the innovation; and relative advantage of the innovation. 

Figure 1: The simplified ADOPT conceptual framework

The more detailed ADOPT model illustrates the interaction of all the different elements of the system. The two left-hand quadrants relate to the time taken to reach the peak adoption level. The right-hand quadrants predominantly influence the peak adoption level, but some also influence the time taken to reach peak adoption through the Relative advantage node and the Short-term constraints variable. 

Figure 2: The detailed ADOPT conceptual framework. 

It is the 22 factors shown in that detailed ADOPT model that we think provides good information on what factors influence adoption. When using ADOPT, a user enters their responses to the 22 conceptual variables into an Excel spreadsheet, which then calculates the predicted time to peak adoption and the peak adoption level. The results are presented as an S-shaped cumulative adoption curve that can then be used to redesign the draft project for greater effectiveness. 

The 22 factors are listed below along with their definitions. You will recognise five factors as they were mentioned by Rogers, but there are others that are also important to consider. 

1. Profit orientation where maximising profit is a strong motivation 
2. Environmental orientation where protecting the natural environment is a strong motivator
3. Risk orientation where risk minimisation is a strong motivator
4. The enterprise scale where there is a major enterprise on a farm that could benefit from adopting the innovation
5. Farms that have a management horizon that is longer term i.e., greater than ten years management horizon)
6. Short-term constraints which is where farms have severe short-term financial constraints
7. The ease with which innovation can be easily trialled on a limited basis before a decision is made to adopt it on a larger scale 
8. Complexity 
9. Observability 
10. Advisory support (paid advisors) that would be capable of providing advice relevant to the innovation
11. Group involvement which is the proportion of the target population who participate in farmer-based groups
12. The relevant existing skills and knowledge 
13. Innovation awareness
14. The relative upfront cost of the innovation 
15. The reversibility of innovation 
16. The profit benefit in years used which is the extent the use of the innovation is likely to affect the profitability of the farm business in the years that it’s used
17. The profit benefit in the future which is the extent the use of the innovation is likely to have additional effects on the future profitability of the farm business
18. The time for profit benefits to be realised 
19. The extent the use of the innovation would have environmental impacts 
20. Time for the environmental benefits to be realised
21. The extent the use of the innovation would affect the exposure of the farm business to risk
22. The ease and convenience of the innovation for the management of the farm.

While those 22 variables seem fairly comprehensive, we wonder to what degree approaches that better involve end-users (such as co-design) would influence the results. Maybe this could have a positive impact on the process of learning about the practice? We do not know of any work that has been done to explore the links between involvement of end-users and learning, so maybe it’s a gap for a budding researcher to explore! 

ADOPT was first released in 2013 and we like the idea of using it to test various extension approaches as part of the design phase of an RD&E project. For instance, if a low score is received for time to peak adoption level, what can we tweak about the project to improve that? It might be as easy as increasing the visibility of the innovation, for example using billboards along the fence-line of the trial plot. The increased observability rating will then increase the awareness score, which in turn influences the time to peak adoption. ADOPT is of course just a model to depict our understanding of a situation. As George Box said back in 1976, ‘All models are wrong, but some are useful’, and we think this is one of the more useful ones!

The diffusion of innovations theory is still central to the thinking we have outlined here. We know that there has been some criticism of the theory, such as its pro-innovation bias and we covered that in a previous episode. We recommend you go back and read that post if you want more details on why we still think this is a useful approach.  

Well, you have read our thoughts, now we would like to hear yours! Add a comment below and tell us your thoughts about the factors that affect adoption or your experience with the 22 factors used for ADOPT. We don’t want this to be just a one-way conversation—join in by sharing your thoughts and ideas with us! 

Thanks folks for reading this Enablers of change blog post. Remember to subscribe to our newsletter if you’d like to know when new episodes are available. And if you liked what you heard, please tell your friends so they can join the conversation!

Resources

Kuehne, G., Llewellyn, R., Pannell, D. J., Wilkinson, R., Dolling, P., Ouzman, J., & Ewing, M. (2017). Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy. Agricultural Systems, 156, 115-125.

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