In an earlier article, I looked at ‘Bankable Signatures’ and whether they were relevant to the possible measurement of trust. The analysis was triggered by a pending Centre for Innovation Industry Economic Research (CIIER) research project looking at the measurement of trust.
The thinking about bankable signatures led to consideration of the linkages between believability, reputation, recognition, and outcome, and how perceptions of trust are impacted by them.
As a general rule, if it is difficult to measure something directly, it is often useful to measure the factors which affect what you are trying to measure, and to measure the variations in those factors, in order to extrapolate likely effects on the outcome.
I first used this approach when extrapolating the likely ICT employment levels that could be predicted from variations in the stock market (as a surrogate for investment intentions). These (retrospectively highly accurate) projections were published in the ACS Statistical Compendia in the years I was its chief researcher.
Meteorology, however, offers us a classic example of this approach. Over a long time, it was observed by farmers and sailors that discernible changes in air pressure and humidity indicated a likelihood of impending stormy weather. A repetition of similar indicators then became the grounds for a forecast. The air pressure changes were able to later be measured by the use of aneroid barometers, which, coupled with well recorded historical outcomes, led to the science of meteorology.
In other words, a factor was identified, variations in that factor were measured and tabulated against actual outcomes, and thus the nexus, or formula, between those variations and the outcome, was able to be calculated.
We know, from experience, that belief in a person or institution creates a propensity for trust in that person or institution, and, conversely, disbelief in a person or institution creates a propensity for lack of trust in a person or institution.
The extremes of these beliefs may lead to ‘cognitive bias’, but the effect is that regardless of whether the initial belief is justified or not, the base point of trust is different for everyone.
Weather systems are somewhat the same. The initial barometric pressure of each impending storm may vary, it is therefore more the variation in that pressure, and the speed with which it is varying, that has become the primary measure that allows for a projected outcome.
In the ICT world, we know that any difference between what the client expects our systems to achieve, and what they actually do, creates either client satisfaction or dissatisfaction.
That is, if our system was trusted to achieve an outcome, but failed to live up to its performance expectations, it is judged a failure. If it is trusted to achieve an outcome and exceeds the client’s expectations (faster, more accurate, etc), then it is judged a success.
The simple barometer measure of trust is therefore the difference between expectation and outcome.
We know, however, that meteorology is more complex than just the measurement of variations in air-pressure.
Just as meteorology had to identify other factors which impacted outcome, so too we have to do with the measurement of trust.
Some of these factors are related to capacity, for example, skill, knowledge, availability, priority, sustainability, affordability.
Others are related to history, for example, reputation, past performance (and even Bankable Signature).
And initial belief is impacted by hope, faith, education, and even perhaps naivety.
We can derive standard measures for many of these factors, and variation measures flowing from these.
Once we have this data, I think that a general equation for their impact upon trust could theoretically be derived.
In the meantime, we can try to ensure that our ICT systems pass the primary barometer test: that our systems do what we said they would do, and do so in a competent, professional way.
ICT outcomes should always meet or exceed client expectations.
That is what creates and maintains trust.