Why Instructions Rarely Get Followed

19 Apr

All situations involve change. Yet most instructions, plans and procedures are static, and bear little resemblance to how frontline workers actually perform and behave. This could be because most instructions, plans and procedures assume that frontline workers passively process data. Instead, frontline workers interact with data in dynamic ways which adapt plans and instructions to meet the challenges of specific situations. This can leave what actually works well in an organisation invisible.

Cognitive psychology has moved away from an information processing perspective (Klein, 2014). Information processing suggests that human beings process information in sequence, tacitly analysing data in a straight line. The data\frame model suggests that the process of making sense of events, and reaching a conclusion, is the product of a dynamic interaction between a person’s frame AND the data. In other words, our conclusions are fluid and exploratory by nature, not fixed.

From the information processing position, data drives the process of reaching a conclusion. A person receives data, interprets it, and then concludes\acts. Top down change can operate on this model. If the strategy is logical and sensible, then people will process it as logical and sensible.

By contrast, the data/frame model argues that the way a person perceives the world, their method of framing situations, interacts with the data in a dynamic process. So, when a situation occurs, what is considered data is influenced by the frames we apply to make sense of events. Equally, the data drives the frame; the way we have made sense of a situation is modified and changed by emerging data. The interaction between the data and the frame produces the data\frame perspective (Klein et al, 2006), outlined below

“When people try to make sense of events, they begin with some perspective, viewpoint, or framework—however minimal. For now, let’s (…) call this a frame. We can express frames in various meaningful forms, including stories, maps, organizational diagrams, or scripts, and can use them in subsequent and parallel processes” (Klein et al, 2006)

And

“…frames change as we acquire data. In other words, this is a two way street: Frames shape and define the relevant data, and data mandate that frames change in nontrivial ways” (Ibid.)

The data\frame model has implications for change and feedback. For example, a project manager can agree with an organisational initiative in her Director’s office. The project manager believes the initiative is logical, and could deliver real benefits (such as reduced errors) to their projects. The project manager has used a frame to make sense of the initiative in a positive way.

However, the project manager then leaves the office and returns to their team. The reaction of other team members, work pressures, new information, all filter emerging data to the project manager. As a result, they begin to update their frame. The initiative could still work, but it’ll have to be tweaked in certain parts, added to in other parts, and abandoned in other parts.

The Director is only aware of the project manager’s recent frame, when they interacted regarding the new initiative. In a short period of time, the situation has changed significantly. Emerging data has altered the project manager’s frame, and the intention is to customise the initiative. If there is weak feedback loops between the Director and the project manager, then the intended adaptions do not get fed back. If the adaptions do not get fed back, then the Director is missing out on valuable data–how their planning interacts with frontline workers and frontline situations. This is a valuable opportunity for the organisation to learn and develop.

Taking a data\frame perspective acknowledges the fluidity of conclusions, opinions and sense making. If the fluidity is acknowledged, then the next step is to set up effective feedback loops between planners and frontline workers. The feedback loops can be used to reduce the gap between “work imagined” and “work carried out” inherent in approaches which assume that frontline workers simply process data. The reduction of this gap is essential to improving forecasting, planning and strategy (see Tetlock et al, 2015). It is also essential to capturing expertise and innovation, as frontline workers successfully adapt formal plans to meet practice based problems. The bottom line is this, adaptions happen fast due to data\frame interactions. Keeping track of these interactions creates a more fluid, safer and adaptive organisation.

Reading

Klein, G. Moon, B. Hoffman, R. (2006) Making Sense of Sense Making 2: A Macrocognitive Model. IEEE. Intelligent Systems. The Computer Society. Vol. 21, No. 5 September/October 2006.

Klein, G. (2014). Whose fallacies? Journal of Cognitive Engineering and Decision Making, 9, 55-58.

Tetlock, P. E. & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. New York: Crown

 

 

 

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