Table of Contents:
Change and Learning
Learning and change, as well as motivation are connected. We are motivated to change because of some gap between our present state and desired state, a physical or mental discomfort. When the tension between the two become so great, action is taken with the goal of releasing this tension. The action, when it comes to projects, organizations and businesses, often shows up as process and procedure and is also the act of learning. The tension can also be released by a shifting of the thought that gave rise to the tension (i.e., what was once thought to be not acceptable is now either accepted or less unacceptable). This change of thought can either be brought about via a shift in the socially accepted norms (of the project or organization) or a generational change in the construct of a work force. There will be a deeper exploration into change management, we just want to highlight a desire for change has a role in learning. Additionally, it should be noted that change we must learn about what we desire to change and why we desire to change, as well as learning required to make the change. These are fundamental to an effective change, for without the knowledge of what we are changing and why we are changing the change will neither be effective nor lasting. It is to that end we will briefly discuss some of the change models as they relate to learning.
Lewin’s Planned Change Model
Lewin’s Planned change model consists of three sections: Unfreezing, Movement, and Refreezing. During the “Unfreezing” section the organization, its behaviors, and structures are examined and why they must change is communicated to the employees.1 The Movement section is when the change is enacted.1 The final stage of Lewin’s Planned change model is freezing or re-freezing depending upon the point in time for the effort. This is where he surmised the change was fully established and the new norms or status quo was established. While Lewin’s change model is commonly used and has been further expounded upon (action research model, as a Positive Change Model), it lacks any true depth and would appear to be based upon an autocratic style of leadership' as the change is not developed by the organization, but set down by leadership personnel (a top down approach to change management).
At this point you are probably asking, “What does this have to do with learning”? While we can learn from any changes, planned or not, how do we know if the change is going to get us to our objective? Lewin’s model makes no mention of quality or assessment points along the course of the change. These sample points or quality check points, provide feedback that would be used to adjust the course of action. As we all know very few changes are instantaneous; therefore sampling of the result of those actions are required. This will serve as input for the next set of actions,
Figure 3.5 The phases of Lewin's planned change model.
building upon what was learned much like an airplane samples present position and makes flight adjustments to accommodate for the external forces impacting the aircraft direction..All of this is part of the learning cycle.
Action Research Model
An Action Research change model differs from Lewin’s Planned change model in that there is a pre-assessment (initial research) and the actions are assessed to provide information to guide follow-on actions.* While this change model promotes learning the learning is post-action due to having no predetermined quality points along its path. This type of change model is developed more toward increasing the general knowledge (systems thinking) that can be applied to other settings. 
Positive Model of Change
While Lewin’s Planned Change Model and the Action Research change model are primarily based on needed improvement the Positive change model primarily focuses on what actions are positive within an organization and attempts to capitalize on those items.' While this type of change management focuses on positive aspects of an organization, which aids in the morale of an organization, as with the previous two we have discussed it is a quantitative feature. This type of change model would be helpful with the experiential aspect of leadership as seen in the leadership equation from Kurt Lewin, in that it would provide positive feedback and promote an environment that promotes productive actions that facilitate continuous improvement.
Change and learning are, or at least should be tightly connected systems, they should be bound together in such a way that one cannot truly occur without the other and produce any lasting results. In chapter two we discussed lessons learned; if we combine that discussion with how change and learning work together, we find ourselves learning and planning changes on a continual basis. This would be the very academic definition of a learning organization. A learning organization neither waits of an issue to implement change nor waste an opportunity for change even during a change itself. As with double loop and triple loop learning the cycle of learning and action are consistent and part of the organization’s structure itself.
To set the groundwork for how to proceed requires exploration of what we presently have in capability, compared to what we need to have to remain competitive or grow. We have already discussed this tension between present state and desired state, but now we need to learn what the present state is along with how we will know if we are on the path for the future state. A brief list is provided below:
■ Hard data - these are hard numbers, metrics that are already or can easily be gathered. The lenght of time it takes to do a certain task, the defect rates, manufacturing parts per million and first pass yeild, are all hard data.
■ Soft data - employee attitude surveys are examples of soft data. Иге information gathered, probably some Likert scale of magnitude is not really data that links to physical phenomenon but something less tangible though perhaps not likely less important.
Figure 3.6 To get a clear picture often requires multiple data streams.
■ Energy data - measures of the work or initiative being explored. This can be applied to projects as well as change initiatives that alter the way the company accomplishes the work.
■ Readiness and capability data - do we have the requisite skills on staff to meet the objective, can we manage and effect the desired change.
■ Political data - is there backing for the change initiative from the organizations power structures.
■ Competency data-are the groups that are involved in the initiative able to meet the demands both in the short term as well as sustain.
■ External data - the perspective of the team members or organization at large of external entities
■ Competitor data - what are the competitors doing, what does this mean for the work we are doing and how we set about doing it.
■ Professional data - depending upon what we seek to change we may need other professional resources and subsequent data.
Understanding Process Information
It is not enough to invoke a process where one is needed. Data collection from the process is how we understand the relevancy of the process as well as the competency of that process. How do you improve a thing when it is not known how a thing
Figure 3.7 Define what matters and track through the changes to assess effectivity.
performs or question what impact our changes have on the item under scrutiny? What needs to be changed? Why does it need to change? To do this, an understanding of this process, the inputs as well as outputs to the next depending work, This is done through something often referred to as Key Performance Indicators or KPI. Notice next depending work, improving this specific process may not be the end goal. For example, perhaps the portion of work presently under review is enough, but the entirety of the work chain is not as it needs to be for the organization.
Understanding the present process capability (in some numeric and measured way) and the desired end state of this process (and the final output of the process chain), it is possible to ascertain what to change to meet this KPI improvement target. However, we need not wait for the output of the entire process chain to determine if the changes being made are having the desired impact on the process stream. To do so requires identification of leading indicators, metrics that make possible a reasonable attempt at prediction. Besides the process information that is available to help understand the situation, and perhaps more important, is that of those using the process. In fact, it is a good start to take time to ensure those that are performing the work understand the tools that follow. This combination can help drive improvement and facilitates a high degree of participation by the team members.
Leading indicators are predictive measurements. There are attributes, typically to inputs, that, when present, will allow us to predict in advance the outcome. For example, consider being on a diet; a leading indicator for weight loss would be caloric intake, below a certain number coupled with an exercise regimen that burns some defined number of calories. When set up correctly, that is the calories burned is greater then caloric intake over time, we can predict weight loss.
Lagging indicators are output focused. In the case of our diet example, a lag indicator would be the weight at any given time or the rate of weight loss over time. Lag indicators require a wait to the final attribute of the system; it is a more certain number, but there is a time dependency and associated investment in the work or effort. Metrics that are predictive may be more difficult to determine but offer the benefit of prediction.