Importance Performance Map Analysis (IPMA)

What is IPMA?

IPMA is useful to extends the findings of the basic PLS-SEM outcomes using latent variable scores.

IPMA contrasts the total effects, representing the predecessor constructs’ importance in shaping a certain target construct, with their average latent variable scores indicating their performance.

IPMA highlights the significant areas of improvement. It contributes to more rigorous management decision-making.

IPMA helps researchers to further explain and discuss the findings for managerial implications. For example, when the plotting result indicates high importance and low performance , it helps management personnels to identify major areas for improvement.

Importance-performance map analysis (IPMA) combines PLS-SEM estimates, indicating the importance of an exogenous construct’s influence on another endogenous construct of interest, with an additional dimension comprising the exogenous construct’s performance in a two-dimensional map.

REQUIREMENTS OF IPMA

First, all the indicator coding must have the same direction; a low value represents a negative outcome and a high value a positive outcome. Otherwise, we cannot conclude that higher latent variable values represent a better performance. If this is not the case, the indicator coding needs to be changed by reversing the scale (e.g., on a 5-point scale, 1 becomes 5 and 5 becomes 1, 2 becomes 4 and 4 becomes 2, and 3 remains unchanged).

Second, no matter whether the measurement model is formative or reflective, the outer weights must not be negative. If the outer weights are positive, the performance values will be on a scale of 0 to 100. However, if outer weights are negative, the performance values will not be in this specific range but, for example, between –5 and 95. Negative weights might be a result of indicator collinearity. In this case, the researcher may carefully consider removing that indicator.

Original Partition of IPMA

IPMA Model

Source: Ringle & Sarstedt (2016)

Output of IPMA

Where are we going to focus on?

it is preferable to primarily focus on improving the performance of those constructs that exhibit a large importance regarding their explanation of a certain target construct but, at the same time, have a relatively low performance

Example of IPMA outputs in SmartPLS 4

CONSTRUCT LEVEL

Note: Total effects (X axis) represents importance, PD (Y axis) represents performance.

INDICATOR level

Note: Total effects (X axis) represents importance, PD (Y axis) represents performance.

Note:

Note that in an IPMA, the direct, indirect, and total effects (just like the latent variable scores) come in an unstandardized form and can take on values much greater than 1. The use of unstandardized total effects allows us to interpret the IPMA in the following way: A one-unit increase of the predecessor’s performance increases the performance of the target construct by the size of the predecessor’s unstandardized total effect, if everything else remains equal (ceteris paribus).

IPMA is not limited to the construct level. We can also conduct an IPMA on the indicator level to identify relevant and even more specific areas of improvement. More precisely, we can interpret the unstandardized outer weights as the relative importance of an indicator compared with the other indicators in the measurement model, no matter whether the measurement model is reflective or formative.

ExerciseS

Exercise 1

Download a project here.

Exercise 2