Participatory prospective analysis (PPA) is a study of future possibilities. As the name implies, this technique uses participation as a step to formulate variables and strategic scenarios that can be taken. PPA is very qualitative in nature, so the respondents involved in this study are really experts or understand their fields. The determination of these expert respondents is the key to the success of PPA (note!).
Prospecting is not the same as quantitative forecasting. Quantitative forecasting generates possibilities based on previous events or data; it does not include strategic actions that can be taken to avoid or lead to a certain condition. PPA, on the other hand, forecasts possible futures while preparing strategic actions and seeing if those actions are a necessity for future change.
There are 8 steps to PPA: (1) define system boundaries; (2) identify variables; (3) define variables; (4) analyze the influence of each variable; (5) identify and select key variables; (6) determine the status of key variables; (7) develop scenarios; and (8) identify scenario implications and related actions.
Expert respondents that we choose according to their fields are given questions about the system that we will discuss, for example, the prospect of chili peppers in Indonesia. Then we ask the respondents to write down the variables that affect the system. Write only one or two words, not paragraphs. Then we collect the variables obtained from the respondents and return them to them to provide a definition of each variable. This is necessary so that there are no differences in perception when making scenarios. After all respondents agree with the definition of each variable, the next step is to determine the key variables by analyzing the influence of each variable. Key variables are variables that have a large influence but little dependence on other variables. Through these key variables, pessimistic, moderate, and optimistic scenarios were developed, and then, in the last step, the actions related to these scenarios were determined.
PPA materials for us to learn together are here:
Defining system boundaries
PPA can be combined with other analytical tools, such as multidimensional scaling. Multidimensional scaling will produce variables called leverage variables that can be used to become variables in PPA. So I can skip steps 1 to 3 here. However, if you use pure PPA, then do participatory according to the previous paragraph that I have explained. You can read the material on multidimensional scaling in my article entitled, Multidimensional Scaling Part 4.
In this exercise, I assume that the variables used to build the competitiveness of chili are 10 (according to what is in the exercise file), namely: land area, productivity, technology, APBD, number of gapoktan, number of extension workers, road damage, imported chili, and chili stocks.
Analyzing the influence of each variable
Of the 10 variables, expert respondents are required to analyze the effect of each variable. If you have downloaded the PPA training material, immediately open the file and look at the “Direct Influence” sheet. This is the only sheet that you need to fill out. The other sheets, including the results, are already running according to the existing formula.
In the direct effect sheet, you fill in the variables in column A or the descending row, and then automatically, the matrix in row 10 will be filled in. Next, you fill in the relationship between the row variable and the column variable. The way to fill it in is between 0 and 3. 0 means no direct effect, 1 means little effect, 2 means medium effect, and 3 means strong effect. You fill it in one way, namely “how much influence does the “row variable” have on the “column variable”. For example, I’ve filled in “land area has a strong effect on APBD” (first row of the APBD column), but APBD has a moderate effect on land area (fourth row of the land area column). This means that the relationship assessed is one-way; if A has a strong effect on B, it does not necessarily mean that B also has a strong effect on A.
Fill in each variable’s influence on the other variables.
Identifying and selecting key variables for participatory prospective analysis
After expert respondents fill in the relationship between each variable, we can determine the key variables for all these variables on the results sheet. Don’t forget that the vertical and horizontal lines are positioned right at number 1 (manually slide a little).
What is meant by key variables are variables in the input quadrant that have a high influence but low dependence on other variables. The stakes quadrant is a connecting variable that has high influence and dependence. While the output quadrant has variables that have high dependency and low influence.
Why are key variables chosen in the input quadrant? Because in this quadrant, the variables have a high influence, adding or reducing these variables will directly affect the results. Secondly, the variable has a low degree of dependency. This means that the cost of changing variables in this input quadrant is lower than changing variables in other quadrants.
In this exercise, it was found that the key variables are technology and land area.
Scenario Building in Participatory Prospective Analysis
The key variables we obtained from the influence vs. dependency quadrant above can be used to develop indicators. Indicators are obtained from each of these key variables by dividing them into three situations: pessimistic (A), moderate (B), and optimistic (C).
An example of compiling indicators is shown in the figure below:
I use another example because the key variable result in the exercise only produces two key variables. Next, notice that among these variables, there are indicators that cannot run simultaneously; we mark them with a red line.
In the image above, indicator A1 cannot coincide with C5, A2 cannot coincide with C4, and A3 cannot coincide with C5.
Example: “If there is no addition of agricultural tools and facilities, it is impossible to make actual productivity equal potential productivity” (A1 cannot coincide with C5).
The scenario is ready to be formed once you have combined the indicators above. Take a look at the following picture:
Scenario development takes into account current conditions and obstacles that may hinder success. It usually consists of several options, such as optimistic scenarios, pessimistic scenarios, and others. Researchers usually give different names to the scenarios, for example: sunrise scenario, hand in hand to prosperity scenario, etc.
Scenario implications and related actions
The final step is to determine the policy implications related to the scenario that has been determined. Implications and related actions are technical steps on how to realize the indicators in the scenario. Take a look at the picture below:
In the policy package options column, we detail what steps can be taken so that the indicators selected in the scenario can be realized.
I emphasize once again that the success of this tool lies in determining expert respondents. This tool is very easy compared to other statistical tools. But the difficulty is to gather expert respondents, who are usually decision-makers who certainly have bureaucracy to be able to meet them. However, this depends on the purpose and topic of the research you are using because this PPA can also be used in smaller scopes, such as organizations or groups.