Impact of Disruptive Technologies
Some notes from a recent project on considerations for development of effective assessment frameworks that capture the impacts of new/emerging disruptive tech.
· Introduction of disruptive technologies can lead to a range of socioeconomic systemic impacts – with both positive and negative being experienced by different parties;
· Focus is often on broad statements of ‘net impact’ that attempts to summarise the overall merit/cost of a project. Such an approach can produce pithy headline figures but often overlooks nuances critical to project success;
· Successful adoption of technologies requires a shared understanding of the intricacies of impacts to key stakeholders, with implementation ensuring that positive impacts are shared, and negative impacts mitigated where possible;
· Impacts can be classified as being either:
· Direct – the cause-effect consequences of the implementation of the technology. This can include the results of investment and spending decisions associated with construction and implementation of the technology (e.g. additional capital expenditure, generation of additional revenues, employment of new labour) and the shifting/mitigation of costs (e.g. costs delayed, incurred by another party or reduced)
· Indirect - results from transactions throughout related supply chains that would not have occurred if not for the implementation of the economy (e.g. purchases from suppliers of new/additional inputs, increased employment by suppliers, collaborators and customers resulting from activities)
· Induced – result from general changes in behaviours resulting from implementation of the technology (e.g. increased investment in local communities, increased productivity/competitiveness in economies, decrease in prevalence of ‘negative’ behaviours)
The Need of a Strong Base Case
· ‘Impact assessment’ implies a measurement of change resulting from some sort of disruption;
· Any assessment of impact requires a strong base-case that articulates the ‘business as usual’ situation – what conditions will likely exist without introduction of the disruption;
· Many impact assessments retrospectively model a hypothetical base case rather than collecting reliable and valid data prior to introduction of the disruption. This diminishes the robustness of the assessment.
Identifying and Testing Areas of Impact
· A robust impact assessment will attempt to consider direct, indirect and induced impacts from a range of different perspectives (e.g. impacted governments, communities, industries, stakeholder groups etc.). This recognises that one group’s cost can be another’s revenue.
· Assessment of each impact should be based upon a strong as possible understanding of causal impacts. In a perfect world we would have strong statistical evidence as to cause and effect of the disruption. In reality, developing an assessment of impact in a timely and efficient manner will likely require articulation of ‘plausible impacts’ suggested by the best evidence available. The assessment would draw from evidence including (in descending order of robustness):
o Statistically proven cause and effect based upon data from the exact context;
o Statistically proven cause and effect based upon data from a comparative context;
o Profiling and contextualising of outcomes from a range of identified comparative benchmark projects;
o Modelling of identified plausible impacts; and
o Modelling of ideal impacts resulting from project goals.
· Whatever the level of assessment, there will always be consideration as to the sensitivity of impacts (i.e. variability resulting from changes in assumptions).
Challenges in Impact Assessment of Disruptive Technologies
· A critical challenge in any impact assessment is determining causation (rather than correlation) of impacts. Often this is not possible within the scope of the Assessment - particularly for indirect and induced impacts. This should be documented with the validity of findings being treated with appropriate caution;
· Areas of assessment can be influenced by the availability of data, with the overall assessment being biased towards areas where data is most readily available;
· Assessments can often simplify the impact of a disruption into linear cause-effect models. In reality, any system contains a wide range of interrelated reinforcing and balancing feedback loops that will ultimately determine impacts. Where possible such feedback loops should be identified and considered in analysis; and
· Many impact assessments are static documents – estimating impacts at a given time based upon available data. These reports are inherently less valuable than systems that allow for ongoing testing and refinement of impact assessment over time.