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What is the Technology Acceptance Model?

We’ve been exploring various change models and in this episode we’re going to look at the Technology Acceptance Model (TAM) and see how it can help us understand why people adopt or resist new technologies.

TAM was developed by Fred Davis in 1986 and is based on the idea that our attitudes towards technology are shaped by two key factors: perceived usefulness and perceived ease of use. Perceived usefulness refers to the extent to which we believe that using a technology will enhance our performance or achieve our goals, while perceived ease of use refers to the degree to which we believe that using a technology will be effortless and straightforward.

Interaction of the elements of the Technology Acceptance Model. Source: Davis, Bagozzi and Warshaw (1989, p. 985).

According to TAM, these two factors are the primary determinants of our intention to use a technology, which in turn predicts our actual usage behaviour. In other words, if we believe that a technology is useful and easy to use, we’re more likely to adopt and use it.

So, how can we apply TAM to our work in agricultural extension? Here are a few examples. Firstly, assessing farmers’ attitudes towards a new technology. Let’s say that we’re introducing a new mobile app that can help farmers track their crop yields and soil health. To assess farmers’ attitudes towards this app, we could survey them on their perceptions of its usefulness and ease of use. For example, we could ask questions like:

  • How useful do you think this app would be for improving your crop yields?
  • How easy do you think it would be to learn how to use this app?
  • How much effort do you think it would take to use this app on a regular basis?

By collecting data on farmers’ attitudes towards the app, we can identify potential barriers to adoption and develop strategies to address them.

A second example is designing training programs about new technologies. Once we’ve identified a new technology that we want to promote, we can use TAM to guide the design of our training programs. For example, if we know that perceived ease of use is a key factor in adoption, we can design training materials that emphasise the app’s user-friendliness and provide clear, step-by-step instructions for its use. Similarly, if we know that perceived usefulness is a key factor, we can highlight the app’s benefits and provide case studies or testimonials from other farmers who have successfully used it to improve their yields.

The third and final example is monitoring adoption and usage rates. TAM can help us monitor adoption and usage rates for new technologies over time. By tracking changes in farmers’ attitudes towards a technology, we can identify factors that may be contributing to increases or decreases in adoption. For instance, if we notice that adoption rates for the crop-tracking app have stalled, we may want to conduct further research to identify specific barriers to adoption and develop targeted interventions to address them.

We found some recent work on TAM. One was in the Australian cotton industry (McDonald et al., 2022) where researchers explored the use of automated technology on-farm and found that social factors and workforce considerations influenced the grower’s motivation to adopt automated technology. Perceived usefulness and ease of use were critical for adoption. In Germany Mohr & Kühl (2021) used TAM along with the Theory of Planned Behaviour to explore acceptance of AI in German agriculture. 

In conclusion, TAM is a powerful tool that can help us as enablers of change understand why people adopt or resist new technologies. By assessing perceived usefulness and perceived ease of use, we can identify potential barriers to adoption and develop strategies to overcome them. TAM can guide the design of training programs and help us monitor adoption and usage rates over time. 

Well, you’ve read our thoughts, now we’d like to hear yours! Add a comment below the blog post and tell us about your experiences with TAM, including any tips and further ideas about it. We don’t want this to be just a one-way conversationjoin in by sharing your thoughts and ideas with us! 

Resources

Davis, F.D. (1986). ‘A technology acceptance model for empirically testing new end-user information systems: Theory and results’, Unpublished Doctoral dissertation, Massachusetts Institute of Technology. 

Davis, F.D. (1989). ‘Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology’, MIS Quarterly, vol. 13, no. 3, pp. 319-40.

Davis, F.D., Bagozzi, R.P. & Warshaw, P.R. (1992), User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

McDonald, N., Fogarty, E. S., Cosby, A., & McIlveen, P. (2022). Technology Acceptance, Adoption and Workforce on Australian Cotton Farms. Agriculture, 12(8), 1180. Available online. 

Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816-1844. Available online.

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Danielle Lannin England
11 months ago

What a great simple tool to use to design and monitor technology adoption – thank you

Thomas Duniya Bidoli
Thomas Duniya Bidoli
11 months ago

Quite logical in the context of adopter behavoiours

Umair Safdar
Umair Safdar
10 months ago

Great and tool with simple explanation. Its seems logical for communication for development interventions.

Andres Jaramillo
7 months ago

Thank you guys for the always interesting blogs/videos/podcasts. All the time I wonder how to apply the discussed topics when working in developing and under-developed countries where the socio-economic makeup of the societies/communities is so different……your discussions are always food for thought though….thanks!

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