The Science of Technology Adoption

The Science of Technology Adoption

David Shaywitz, a physician-scientist turned venture partner, lamented the tyranny of the dashboard not long ago. In his excellent piece on healthcare innovation and the technology adoption life cycle, David is concerned that the ideology of big data − with the dashboard as a key symbol and feature, has taken on a life of its own, assuming a sense of both inevitability and self-justification. From measurement in service of people, we measure in service of data. The risk is measuring much that is unimportant while not capturing what is important, including subjective, expert judgment.

As an independent consultant to companies developing consumer-facing digital health applications, I see the tyranny in new form factors like the dashboard, but also in the expectation that clinicians and patients adopt a new tool from day one. Build it and they will come is the mantra.

But when people are busy and unfamiliar with a product, why would they come? There’s nothing positive that awaits, or what psychologists call positive immediate consequences (PICs), a.k.a. rewards, such as a beer, pizza party, chocolate, or stars (for those of us under the age of 6). Really, there are only negatives (NICs): normal routines are disrupted; people have to make an effort learning something new; risk getting it wrong or not getting it at all; be reprimanded by a supervisor; remember a new login, et cetera. Interestingly, in Dutch (my native language) “niks” means ‘nothing’.

Three things to keep in mind in the technology adoption life cycle:

It must be fun, positive and rewarding to use in order for technology adoption to work.

Behavior science teaches us that life is a PICNIC when you understand behavior. A good bit of behavior is driven by its consequences. When something positive awaits, that motivates and increases the likelihood of the desired behavior. This holds for pigeons as well as higher-order animals like humans. It‘s how animals are trained for dog and pony shows, movies, and medical exams (just ask the lions in a zoo). But even when there’s a cookie around every turn and the animal is motivated, establishing a new routine takes a disproportionate amount of effort. Put simply, it is easier (positive) to keep the old routine and unclear (negative) what the new routine will bring. Chances of giving up, whether a new diet or computer dashboard, outweigh the odds of technology adoption.

Expect a learning curve, no matter how user-friendly your form factor is.

To the user, it is new technology.

Cognitive science shows that it takes many repetitions, practice if you will, to go from novice to expert. So even when you are eager to learn something, such as driving a car or hot computer game, new information and routines must be learned, integrated and automated in our brains and bodies. It takes time to develop muscle, motor and neuronal memory, all of which contribute to a sense of mastery and skill. A novice user cannot be an expert or comfortable on day one. Expertise and familiarity require repetition, lots of it. 

For technology adoption to work, it better be good.

Thanks to implementation science, we know that there are 8 pieces to the implementation puzzle:

  • Acceptability
  • Technology Adoption
  • Appropriateness
  • Cost Impact
  • Feasibility
  • Fidelity
  • Penetration,
  • Sustainability

For present purposes, adoption applies to individuals as well as organizations and has everything to do with the intention to use. In this sense, technology adoption can be easy, unless different people have different intentions. I personally see this all the time, for instance in administrators versus staff. The former tend to push for a technology; the latter resist. Acceptance on the other hand, has everything to do with the perception that an innovation is agreeable, palatable and satisfactory. Technology acceptance, or lack thereof, drives technology use.

Finally, usability engineering, teaches us that the intention to use a technology rests on two core concepts: perceived ease of use (i.e., usability) and perceived usefulness. Usefulness is all about achieving desired goals. Be faster, more accurate, detailed, et cetera. Understanding user goals, therefore, both on an individual and group (stakeholder) level, is key before building a system.

Conversely, outlining system goals, what it can and cannot do, is critical once the system is built. This sets the right expectations and avoids people getting frustrated looking for things the system will not do. Adoption, moreover, depends on the advantage of the innovation compared to existing methods of achieving a goal or activity. All too often, innovations do not add much or the advantages are unclear.

How Does Adoption Work?

  1. Use good, user-centered design to spur initial excitement, motivation and intention to use. Match the system to user needs and goals.
  2. Confirm usability through small tests with end-users: remove user interface and interaction barriers.
  3. Make system interactions positive and rewarding, time and time again.
  4. Give opportunity to practice, until the user expresses or demonstrates mastery.
  5. Offer initial training and support to synchronize the goals of the system and user.
  6. Monitor and reward use, over and over again. To level it up: consider pairing the reward with a new bit of information (‘tip’) that builds on the activity just completed. Not only does this acknowledge an accomplishment; you’re also expressing confidence the user can do it again by revealing the next step. Call it empowerment. Be sure the tip is sweet and easy (positive), or you risk bogging down and frustrating (negative) the user.
  7. Provide ongoing support.

Behavior science is a tad more intricate than presented here. For those interested in a quick primer on behavior analysis, consider Life’s a PIC/NIC… when you understand behavior, by Aubrey Daniels & Alice Lattal (2017, Sloan Publishing).

Chantal Kerssens understands how people think, make decisions, and behave. And she knows how people interact with new health technologies. Contact Chantal for all of your digital health application needs. 

Further reading:

●     Proctor, Silmere, Raghavan et al. (2011). Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda. Adm Policy Ment Health (2011) 38:65–76. DOI 10.1007/s10488-010-0319-7.

●     Nielsen (1993). Usability Engineering. Morgan Kaufman/Academic Press.

●     Fisk, Rogers, Charness et al. (2009). Designing for Older Adults – Principles and Creative Human Factors Approaches. CRC Press.

●     Venkatesh, Morris, Davis & Davis (2003). User acceptance of informatio

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