My days constantly revolve around finding new ways to gather and interpret insights which will improve our understanding of our users: what they like and don’t like, what they’re using or not, and why that might be the case.
The need to answer these questions and many others have driven me to revisit a time when I was still at university conducting my own study into the adoption of online technology by health professionals. My work there taught me that to understand how and why an individual chooses to adopt or reject an innovation, one must first understand the underlying factors that influence their decision. Knowing that countless studies have been dedicated to extensively examining these factors and carefully formulated into various prominent adoption and diffusion theories, I decided to see what the application of age-old principles and the re-analysis of honest user feedback could tell us.

A little about our users and technology within healthcare environments

We’re privileged to work with Governments, hospitals, health professionals, community service providers, researchers and health sector experts over the world to create better outcomes for patients and drive greater efficiencies for health services. Our users want answers quickly, easily and provided in a way that gives them the ability to act and make intelligent decisions to positively impact the experiences of their patients. However, all product companies, like ours, are well familiar with the complexities associated with working with diverse user bases in healthcare settings. 
Recent years have seen numerous technologies become widely adopted in healthcare environments in an effort to improve quality and increase the efficiencies of care delivered to patients. More traditional evaluations of healthcare-centric technologies have focused on the implementation of a particular technology, with positive outcomes associated with the successful integration of it into clinical environments. Failure to do so has often been attributed to a lack of buy-in, slowed development, and the poor assessment of the need for the solution among many others. But, what does this all actually mean in practice? Is that all that there really is to know? And more importantly, what can we actually learn from it?

Trying to find answers in the Literature 

Numerous studies have found personal factors, innovation characteristics, and facilitating conditions all shape an individual’s ultimate decision and persistence with a new technology (Adler & Clark, 1991; Rogers, 1995; Straub, 2009). Together these social interactions influence the process of technology adoption and acceptance (Hord, Rutherford, Huling-Austin & Hall, 1987). 
Individuals, such as health professionals, construct beliefs about technology based on a variety of factors. These beliefs are malleable and have been shown to be shaped by prior experiences (Agarwal, Sambamurthy & Stair, 2000); perceived ease of use and usefulness (Davis, 1989); personality traits (Agarwal & Prasad, 1998a; Wood & Swait, 2002); structured educational experiences (e.g.,conferences, seminars) (Agarwal & Prasad, 1998b); and societal pressures (Hord et al., 1987). Within healthcare, the significant effect of social influence on the adoption behaviour of health professionals has been noted in prior studies (Fitzgerald, Ferlie, Wood & Hawkins, 2002). 

Combining what we’ve learned with what we know

Over time, we’ve seen what works, what doesn’t, and realised while many factors cannot truly ever be overcome they can be mitigated if the appropriate initiatives are put in place. 

Lesson 1: Create positive feedback loops that reinforce social influence and the need for your tools

It is important to understand the effects of social pressure/impact: the extent to which someone feels and is influenced by social pressure to use technology (Fishbein & Ajzen, 1975; Venkatesh et al., 2003). We see many client programmes launch with low involvement from key stakeholders early on. The best and most frictionless programmes allow users to be continually involved during the planning and implementation phases in order to achieve support for the new programme and encourage uptake.

Lesson 2: Emphasise the importance of supporting change from the top-down

The next lesson may seem obvious but something we do encounter quite often. Attending compulsory training sessions and being asked to read up on software manuals have become a fait accompli for many health professionals already tasked with an immense amount in their everyday lives. This often leads to embittered staff and reluctant staff before they’ve seen the awesome stuff that your technology can do. Facilitating conditions encompass the extent to which an individual believes that their organisation is supporting change toward a new technology (Venkatesh et al., 2003).  While dissenters and slow adopters will always be ever-present, it is important to ensure that you have programme champions from various backgrounds and teams. Programmes borne out of one group within an organisation will always encounter resistance as they try to expand if they don’t have widespread support at the Leadership level and collaborative partnerships at the very start. This should be in place or encouraged at the start of your planning process. 

Lesson 3: Encourage your users to utilise your tools creatively and in unintended ways 

We’re focused on being the world leader in patient-reported measures and making beautiful, powerful reporting that’s simple and easy for our users to understand. 
We all know that you need to create products that help your users to perform their given tasks and are super easy to use to be completely ‘free of effort’. This is not only obvious but well-validated throughout prominent in adoption theory (Davis, 1989; Venkatesh et al., 2003). Even so, we’ve learned that users will think that your tools are not easy to use and not useful if you have only shown them how your tools work and not shown them how to use our tools to creatively solve their problems in ways that may not have been initially anticipated. Juggling the balance between making a good product and good client decisions has led us to reinvest more time and effort into re-shaping our product education and the way we utilise client feedback to recreate the vision we have for our product. Decisions we believe will allow us to develop a much more impactful and compelling offering with each iteration of our tools.

References

  1. Adler, P. S. Clark., K.B. (1991). Behind the learning curve: A sketch of the learning process. [Article]. Management Science, 37(3), 267-281. 
  2. Agarwal, R. P., J. (1998a). The antecedents and consequents of user perceptions in information technology adoption. Decision Support Systems, 22(1), 15-29. doi:10.1016/s0167-9236(97)00006-7 
  3. Agarwal, R. P., J. (1998b). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215. doi:10.1287/isre.9.2.204 
  4. Agarwal, R. S., V. & Stair, R.M. (2000). Research Report: The Evolving Relationship Between General and Specific Computer Self-Efficacy–An Empirical Assessment. [Article]. Information Systems Research, 11(4), 418. 
  5. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. [Article]. MIS Quarterly, 13(3), 319-340. 
  6. Fishbein, M. A., I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley Publishing Company. 
  7. Fitzgerald, L., Ferlie, E., Wood, M., & Hawkins, C. (2002). Interlocking interactions, the diffusion of innovations in health care. Human Relations, 55(12), 1429-1449. doi:10.1177/001872602128782213
  8. Hord, S. M. R., W.L.; Huling-Austin, L. & Hall, G.E. (1987). Taking charge of change. Alexandria, VA: Association of Supervision and Curriculum Development. 
  9. Rogers, E. M. (1995). Diffusion of Innovations, 4th Edition: Simon & Schuster. 
  10. Straub, E. T. (2009). Understanding Technology Adoption: Theory and Future Directions for Informal Learning. [Article]. Review of Educational Research, 79(2), 625-649. 
  11. Wood, S. L. & Swait., J. (2002). Psychological indicators of innovation adoption: Cross-classification based on need for cognition and need for change. [Article]. Journal of Consumer Psychology (Lawrence Erlbaum Associates), 12(1), 1-13. 
  12. Venkatesh, V., Morris, M.G.,Davis, G.B. and Davis, F.D. (2003). User acceptance of information technology: Toward a unified view.[Article]. MIS Quarterly, 27(3), 425-478.