Multi-channel provisioning of public services - Department of ...

will increase, which means that the perception of having a better service delivery will never change
(van Deursen, 2007).
Figure 2 illustrates the optimality of a system as a function of time and implemented changes: The
desired configuration of resources, systems and processes is constantly high and aligned to the optimal
possible configuration, while the optimality of the actual configuration declines over time, even with a
stable configuration. Examples of factors contributing to this change are the leverage of resources and
scalability of the solution, changes in the technological affordances through the availability of new
channels or technology or the change of regulations and required service level.
Figure 2 Alignment of desired and actual system configuration over time
From the point were it is decided to align the actual and desired service configuration (squares), there
will still be some time before the new configuration is available for use. From the time of launch of the
new system of method, there will be some time before the organisation is adjusted to the new reality.
Due to this, there is already a discrepancy between the desired and actual configuration (circles). To
cope with this, the time between reengineering can be shortened or one could facilitate for user-driven
innovation and process management, which might allow a more continuous synchronisation of the
desired and actual system configuration.
One should be aware of that the political dimension of the perceived optimality of a current
configuration is multi-faceted. However, this situation is not specific from government organisations
per se, as the possibility of hidden agendas is present in any organisation. The perceived optimality
described in Figure 2 is not subject to individual reasoning but based on goals stated at the point where
the pursued configuration was initiated.
If the innovation is user-driven, the users should be able to fully describe the process change in such a
manner that the model as far as possible represents the new reality. It should also be possible for the
user to see the consequences of the requested change (run what-if scenarios on existing data with new
rules or alternative process flow) and be able to request changes within the defined laws and flexibility
of the process execution platform.
We distinguish between process innovation and process flexibility. Where process innovation can be
the continuous optimisation of processes and synchronising the local best practice to the formal
documented and system supported process, process flexibility is the possibility for tailoring one single
process instance to the local requirements of that instance. This could include adding or removing
milestones, process actors or similar phenomena for one single process instance. It would also be
feasible to differentiate process innovation from process invention, where process invention is the
creation of novel approaches, which do not become innovations until others adopt them. This being
said, provisioning of best practice need not only be top down but also between peers providing similar
services, in different government organisations.
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