Diagnose
Run the 16-item UTAUT pulse survey below with every affected team. Add five short interviews per site to hear the reasons behind the scores.
Output: construct scores by team, role and site
Part one · The model
Watch the theory assemble
Venkatesh, Morris, Davis and Davis (2003) tested eight competing acceptance models in four organisations, kept what predicted behaviour, and fused it into one. Press Replay the build, then tap any box for the practitioner's view.
Part two · The practitioner's lab
Diagnose a rollout before it stalls
Score each driver the way staff would on a 1–7 survey (4 is neutral). The lab shows how many people are likely to intend to use the system, how many will actually use it, what the idle spend costs you, and which lever to pull first. Start with one of the example cases.
Intend to use it0%
Actually use it, week in, week out0%
uses itintends to, but doesn'tnot on board
Spend sitting idle each yearR0
The lab uses illustrative weights drawn from the direction of the published findings. It teaches the logic of the model. For a real client, calibrate it with your own survey data and system usage logs.
Part three · The playbook
Five moves from survey to sustained use
A twelve-week rhythm IALE uses when a client has bought the technology but the people have not yet bought in.
Read the pattern
Plot the four means. Anything under 4 is a bottleneck. Compare intention with actual log-ins: a big gap points to facilitating conditions.
Output: one-page adoption heat map
Design levers
Pick one lever per weak driver, not ten. Give each an owner, a date and a measure the CFO would accept.
Output: adoption action plan with budget
Pilot with champions
Start with two teams and their champions. Fix the friction they hit every Friday. Share their wins in the Monday meeting.
Output: pilot results and peer stories
Measure and scale
Re-run the survey and pull usage logs. Scale what moved the numbers. Drop what did not.
Output: before-and-after report, scale-up plan
Part four · The instrument
The 16-item pulse survey
Adapted from the original UTAUT items. Replace “the system” with the real name. Use a 1–7 scale from strongly disagree to strongly agree, keep responses anonymous, and tell staff how the results will be used.
| Driver | Code | Statement |
|---|
Part five · Beyond the core
When the model needs more variables
UTAUT2 (Venkatesh, Thong & Xu, 2012) added three drivers for settings where people choose and pay for technology themselves. Studies of generative AI at work and in universities often add trust and risk as well. This introduces another variable to the elements, thereby shaping new outcomes.
UTAUT2 additions
Hedonic motivation
Is it enjoyable?
Fun and curiosity drive use, especially for consumer-style tools and early AI experiments. Design the first session to feel like discovery, not compliance.
Price value
Is it worth what it costs me?
Matters when staff carry the cost, for example data bundles on personal phones. A R29 weekly bundle can decide whether a field app gets opened.
Habit
Do I do it without thinking?
Once use becomes automatic, intention matters less. Build the tool into a fixed routine, such as the Monday stand-up or the daily safety brief.
Common additions in AI-era studies
Trust
Do I believe the output?
Staff use AI assistants when they know where answers come from and when to check them. Show sources, show limits.
Perceived risk
What happens to my data, or my job?
POPIA, confidentiality and job security fears suppress use. Answer them in writing before the rollout, not after the rumours.
Anxiety
Will I look foolish?
Fear of getting it wrong in front of colleagues slows older and senior staff. Private practice time and small-group coaching help.
Potato check
Three ways a UTAUT study can mislead you
- Intention is not use. People say yes on a survey and still go back to Excel. Always pair the survey with system logs.
- The moderators are uneven in practice. Large meta-analyses (Dwivedi et al., 2019; Blut et al., 2022) confirm the core paths, yet many studies drop or only partly test the moderators. Treat age, experience and mandate effects as hypotheses to check in your own organisation.
- It explains acceptance, not design. UTAUT tells you which driver is weak. It does not redesign the workflow for you. That still takes process work, user testing and budget.
Ethics note: survey responses are personal information under POPIA. Collect consent, report only at team level (minimum five respondents), and never use scores to discipline individuals.
Your turn · discuss
Put your own workplace on the table
1Name a system your organisation paid for that people avoid. Which of the four drivers is weakest, and what evidence do you have?
2Where is use mandatory but intention low? What happens to data quality when people comply without believing in the tool?
3If you had R100 000 to lift adoption next quarter, which single lever would you fund, and how would you prove it worked?
Sources
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
- Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
- Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.
- Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.
- Blut, M., Chong, A. Y. L., Tsiga, Z., & Venkatesh, V. (2022). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): Challenging its validity and charting a research agenda in the red ocean. Journal of the Association for Information Systems, 23(1), 13–95.
- Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students' acceptance and use of technology. Interactive Learning Environments.