Government agencies in most countries are required to produce some sort of list (schedule) of the outputs they are going to produce. Outputs are the final goods and services produced by an agency. One example of this type of document is the Statement of Service Performance (SSP) that New Zealand government agencies need to produce. This document complements the high-level strategic intent documentation called the Statement of Intent (SOI) which sets out the agency's overall strategic intent, outcomes and other material. (See here for an example of how the DoView Outcomes Theory approach can feed directly into the preparation of high-level Statement of Intent-type documentation).
In the Statement of Service Performance (SSP), a target is struck for each of the items in the list of output measures (indicators) the government agency is going to produce. The agency then reports in its annual report on whether or not it has actually met these targets.
Outputs-type documents such as these typically consist of long, hard to process lists of indicators. In most countries or states a central agency of some type has discussions with the specific agency to ensure that its output indicator list is comprehensive. When conducted in the abstract (not against a visual DoView-type visual model of the particular part of the agency's work) these discussions have a very high cognitive load (the amount of mental effort required to undertake a task).
Below is a very small section of a Statement of Service Performance for a department that has responsibility for national emergency management. This particular part focuses on increasing the public's awareness of what to do in an emergency.
Technical discussions with the central agencies when they are reviewing such outputs documentation focuses on: 1) whether the document provides a comprehensive set of indicators on the topic; and, 2) how far up the causal chain running to high-level outcomes the indicators reach. In general, the higher up the causal chain the better. However, outputs are limited to things that an agency can control so that, when measured, they are easy to attribute to the agency. This avoids disputes as to whether the agency should be held accountable for producing them. Central agencies will want to push the agency as far up the causal chain as possible but both parties will usually want to limit the indicators just to things that are controllable by the agency because of the attribution issue.
When there is any complexity around a topic (e.g. many things are being done and there are many other factors affecting high-level outcomes), these output-list discussions can become protracted and confusing. What is being expected of the people involved in these discussions is that they will be able to hold in their heads a mental model of what is being attempted by the agency in the particular area. They then need to compare the proposed list of indicators with their mental model and use this to identify any gaps in the indicator list. This is a daunting task in any area where there is even a degree of complexity.
It is far easier to avoid these mental gymnastics by simply visualizing what the agency is attempting to do in the form of a visual DoView. Once this has been done, proposed indicators can be put next to the boxes set out in the DoView. This will show exactly what is, and what is not, being measured. Further clarity is obtained by marking up those indicators which are controllable by the agency and those which are not-necessarily controllable. In the DoView Outcomes Theory approach the convention is used of marking controllable indicators with an @ (showing that in normal circumstances they are direct accountability indicators). Indicators marked-up in this way are the indicators that be included in the outputs list.
The efficiency of this DoView approach was illustrated in the case of an outputs discussion with the executive of a government agency done by using DoView modeling of the agency's indicators. A senior member of the executive described the hour the executive spent on this discussion as 'one of the most productive hours of my working life'. He was contrasting the efficiency of the way the discussion was handled to the usually tedious and painful discussions about indicators which result from not discussing them against a DoView-type visualized model.
The DoView underlying the outputs list shown above is set out below:
The marked-up DoView shown below illustrates how indicators can be mapped onto the DoView. With this DoView visual approach it is easy to identify what is being measured, what's not being measured and the level at which it's being measured. The same DoView can be use to identify higher-level measures, which are not controllable and therefore should not appear in the SSP but should appear in the higher-level SOI-type documentation which focuses out higher-level outcomes and measures. The DoView approach also works well in cases where both high and low-level information is included within the same documentation.
Below is a more detailed set of indicators from the same Statement of Service Performance, this time the list focuses on emergency response readiness, response and recovery.
The following DoView shows how some of these indicators can be set out against the visual DoView. In addition it shows how results can be traffic-lighted against the DoView if, as sometimes happen, people wish to work within he DoView rather than just from a tabular indicator list when reporting results.
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