Organizational Decision Making: Situational/Contextual Frameworks

Organizational Decision-Making: Situational/Contextual Frameworks
Different contexts call for different leadership approaches. For this reason, Snowden and Boone (2007) emphasize the importance of recognizing the context at a specific time before deciding what action or decision to take. As a guide, the authors developed the Cynefin framework, which categorizes the issues that leaders face into four contexts defined by the nature of the cause-and-effect relationship: simple, complicated, complex, and chaotic. This text analyzes the applicability of different decision-making approaches in each the four contexts.
Applicability of Tools of Rational Economics
Rational economics is an economic theory that assumes that individuals will always make decisions that offer the highest possible level of utility given the choices available (Bruce, 2016). The cornerstone of rational economics is that individuals have perfect information about all available alternatives and their features (Bruce, 2016). Under rational decision-making, an individual identifies a problem, establishes decision criteria, generates alternatives, weighs alternatives, and then selects the best alternative from the available choices (Bruce, 2016). This process may work in the simple and complicated contexts, but may be irrelevant in complex and chaotic contexts.
In a simple context, cause-and-effect relationships are clear and the best decision is easily identifiable (Snowden & Boon, 2007). The team members thus have perfect knowledge about the available alternatives and they only need to categorize and implement the same. In a complicated context, cause-and-effect relationships are not necessarily clear to everyone, although there are several likely solutions to the problem (Snowden & Boon, 2007). In this case, team members could obtain all relevant knowledge through research to accurately establish cause-and-effect relationships, and then choose the best alternative.
For the complex and chaotic situations, however, cause-and-effect relationships are either unclear or non-existent, making it impossible for team members to identify or even weigh different alternatives as is required in the rational model. At least one right decision exists in a complex context, but since participants lack perfect information on cause-and-effects, the decision chosen may not necessarily be the best decision. Complex contexts are characterized by turbulence that makes it impossible to establish causes and effects and hence, identify the best alternatives.
Human Behavioral Responses and Organizational Discipline in each of the Frameworks
Behavioral theories suggest that humans behave a certain way when faced with stressors. However, organizational discipline emphasizes the idea of exercising restraint and learning to follow the best course of action even when that action is not in line with one’s desires (Miner, 2007). The idea of organizational discipline may work effectively in the simple and complicated contexts because cause-and-effect relationships are clear, and all members of the team understand what they need to do. There is a clear sense of direction, making it easy for team members to abide by organizational rules and guidelines. With complex and chaotic contexts, however, it may be difficult and at times impossible to observe the principle of organizational discipline. To begin with, cause-and-effect relationships are unclear and decisions are marred by high levels of uncertainty. It is difficult to anticipate the outcomes of one’s actions in these contexts, making it harder for team members to stick to the laid out guidelines and frameworks. In most cases, team members will be forced to adjust or adapt their decisions as the problem unfolds.


Organizational Leadership and Decision-Making in the Four Contexts
Good leadership requires openness to change. A truly effective leader knows not only how to identify the context of a problem, but also how to change their actions to fit into that context (Snowden & Boone, 2007). In simple contexts, cause-and-effect relationships are evident and known by all team members. Both the leader and the rest of the team have access to the information needed to deal with the situation. The leader thus leads through command-and-control. They issue straightforward directives and delegate authority as all team members understand what they need to do to achieve the desired end-result. In the simple context, therefore, the leader does not need to engage in exhaustive communication with subordinates because disagreements about what needs to be done rarely occur (Snowden & Boone, 2007).
In a complicated context, cause-and-effect relationships are clear although not everyone can see them (Snowden & Boone, 2007). As such, differently from a leader in the simple context who must assess, categorize, and respond; one in a complicated context needs to assess, analyze, and respond (Snowden & Boone, 2007). Analysis requires expertise and as such, a leader in a complicated context may have to exhaustively engage experts in consultations to obtain the best solution.
Complex contexts are characterized by at least one right answer, although the same cannot be ferreted out (Snowden & Boone, 2007). Under the complex domain, one can only understand why things happen in retrospect (Snowden & Boone, 2007). As such, the best approach is safe experimentation, which involves trying out a possible solution, then stepping back to see how patterns emerge before choosing the most appropriate solution (Snowden & Boone, 2007). The effective response, therefore, is to probe, assess, and then respond (Snowden & Boone, 2007). The traditional command-and-control style may not work in complex settings as team members are unsure about what needs to be done and the likely results of the same. Attempts to exercise excessive control or impose order will increase the chances of failure as team members may be less likely to act faced with uncertainty.
Finally, in the chaotic context, no manageable patterns exist. In this context, the leader’s role is to establish order, identify areas characterized by the least levels of turbulence, and then work to transform the situation from chaotic to complicated (Snowden & Boone, 2007). In most cases, chaotic situations are crisis situations - to manage chaos, the leader may need to put in place a crisis management team led by a reliable crisis manager to develop strategies, chart a way forward, and communicate.
Use of Big Data and Data Analytics to Bring Clarity
Big data and data analytics helps to bring more clarity into the process of organizational decision-making. Big data is characterized by huge quantities of data coming from a variety of sources with a high speed of generation (Steward & Cavazos, 2019). Through reading and conducting online searches, an organization can obtain an unprecedented amount of information on the potential causes of a certain problem, the various alternative solutions, and the best solution. For instance, the process of rational decision-making requires one to accurately identify the problem, identify alternatives, and weigh various alternatives to obtain the best possible solution. Big data makes it possible for team members to identify alternatives from resources available online and to weigh alternatives by assessing the levels of success realized by organizations that have adopted each alternative before. This provides room for more accuracy in the decision-making process and increases chances of obtaining the best solutions. Big data and data analytics, therefore, facilitates the process of rational decision-making even in complex and chaotic contexts as it serves as a source of knowledge.
References
Bruce, P. J. (2016). Understanding Decision-Making Processes in Airline Operations Control. New York, NY: Routledge.
Miner, J. B. (2007). Organizational Behavior 4: From Theory to Practice – Volume 4. Armonk, NY: M.E. Sharpe.
Snowden, D. J., & Boone, M. E. (2007). A Leader’s Framework for Decision-Making: A Leader’s Framework for Decision-Making. Harvard Business Review, 85(11), 68-76.
Steward, D., & Cavazos, R. (2019). Big Data Analytics in US Courts: Uses, Challenges, and Implications. New York, NY: Springer.