System Thinking and Causal Loop Diagram (CLD)

In the last 60 years, system thinking has been evolved and is frequently having more impact on science (Haraldsson, 2004). System thinking works like a microscope that reflects a comprehensive, universal view of the system connectivity by modeling and shows the whole system behavior (Richmond, 1994). By applying this method, researchers can describe and illustrate system behavior (Meadows, 2008).

In the human environment and nature, everything is connected with some interactions.

According to Rosnay (1979), a system is a web of various variables correlated and connected within causal relationships and represents some behavior and function, which can solely be identified by observation as a whole. System thinking works as a language for talking about interdependent and complicated results managers challenge every day.

So, system thinking helps researchers to understand the relation of different elements of a complicated problem. In order to do so, the causal loop diagram can be used to develop a qualitative theoretical model.

When humans see an issue or problem, they ask questions. When we want to know a process, we ask questions. Questions like; why is this occurring? How is this issue happening? How to solve it? etc. The causal loop diagram (CLD) can reflect these questions (Haraldsson, 2004). In the past, humans always wanted to promptly settle a cause for any effect that they thought is a problem or issue (Eren Şenaras, 2017). The systems thinking theory and method presents a tool for understanding these questions (Eren Şenaras, 2017).

A causal loop diagram aids in showing and reflecting how various effects in a scheme are interrelated. According to Roberts (1983), the causal loop diagram theory is shown in the table below:

Causal loop diagram signs (Roberts, 1983).

Explanation of the sign Sign

The arrow illustrates a causality. An element or factor causes a change to the factor at the end of the arrow.

A plus sign close to the arrowhead means that the factor at the tail of the arrow and the factor at the head of the arrow change in the same way.

A minus sign close to the arrowhead shows that the factor at the tail of the arrow and the factor at the head of the arrow change in the opposite way. If the tail increases, the head will decrease, and if the tail decreases, the head increase.

The letter R in the middle of a loop shows that the loop reinforces an effect and behavior in the same way and direction, causing either a normal growth or decline.

The letter B in the middle of a loop shows that the loop balances and moves the system in the direction of equilibrium or a fluctuation around an equilibrium point.

To illustrate the CLD method’s functionality and the steps, a simple example is given by Roberts (1983), a reinforcing system of the population.

Example of a causal loop diagram for the population system.

This figure shows the system of the population. As can be seen in the figure above, two variables or factors are affecting the population, which are birth and death. The steps to connect the links and draw the loops these steps have to be taken.

Steps for draw and designing a causal loop diagram for the popula-tion system.

On the other side, to balance the population increase, the variable death is affecting the population. The figure below illustrates the process and how the variables are linked together.

Steps for draw and designing a causal loop diagram for the popula-tion system (the right side).

So, in the given example, two variables were balancing the whole simple. In most cases, while in the first phase, a variable is reinforcing the system, another variable will balance (Haraldsson, 2004).

So CDL can be considered as sentences that are built by recognizing the critical variables in a system and showing the causal relationships within the variables by links.

Finally, a brief story about the desired outcome or the problem can be created by connecting several loops. According to Haraldsson (2004), to build CLD, the following steps need to be taken:

1. Determining the goal or the problem: First, determine what is the goal or the problem? In this thesis, the goal is to study the factors affecting API monetization strategy.

2. Asking questions: Define precisely what you want to answer in the desired goal or problem. Also, considering that there might be many questions for one purpose or one problem. Thus, this thesis’s questions are: what are the main factors that affect the API monetization strategy? How these factors are affecting each other and the main goal?

3. Sorting the main actors: This step is about generating a list of suitable and related variables linked to the question and then sorting them in a hierarchical form and order based on their importance. To do so, first, create a long list of

variables you think might be essential to answer the question and then take away the insignificant ones. According to Haraldsson (2004), it is better to start with maximum 8-10 variables initially.

4. Beginning with a simple CLD: In this level, draw the connections and the links between the variables you chose. Create a loop at the beginning and verify if it is feasible. Proceed with the rest of the variables. While drawing, it can be seen that some variables are missed, but it is normal.

5. Creating an RBP: RBP stands for the Reference Behavior Pattern to describe the behavior of the variables and the model. An RBP is a graphical description of the variable’s behavior over time. RBP is used to outline the initial perception of the system. An example of an RBP is shown in the figure below. In the population example, the “health” as a variable can be described as presented in the figure.

To create an RBP graph, some historical and numeric background might help.

This historical background is called an observed behavior pattern or OBP.

Observed behavior pattern and reference behavior pattern of pollu-tion example.

Also, the slope of the loop is less critical. As shown in the figure above, the “health”

variable decreases because of pollution. The right-hand side graph estimates historical data about health, and the left-hand side graph shows if it is increasing or decreasing.

Causal loop diagram for pollution example.

6. Testing the CLD model: When the first CDL is ready to verify if it is logical, asking an expert to give, feedback might help, and use the understanding based on literature

7. Learn and revise: The CLD is never accurate on the first try, and it is an iterative method. So, it is important to review it and redo it again.

8. Conclude: After many iterations of CLD, the final version of the causal loop diagram should answer the primary question.