Importance-performance Map Analysis (IPMA)
Standard PLS-SEM analyses provide information on the relative importance of constructs in explaining other constructs in the structural model, which is valuable for drawing conclusions. Importance-performance map analysis (IPMA) extends these results by also taking the performance of each construct into account, so that conclusions can be drawn along two dimensions: importance and performance. This is particularly useful for prioritizing managerial actions, since it is generally preferable to focus on improving constructs that are highly important for explaining a target construct but currently show relatively low performance.
Understanding IPMA
Hair et al. (2024) explain the IPMA in more detail; also see, for example, Höck et al. (2010), Ringle and Sarstedt (2016), Rigdon et al. (2011), and Schloderer et al. (2014) for applications. Take a look at a small Excel example on how to use the IPMA SmartPLS results for creating an importance-performance map illustration (scroll to the end of the list).
IPMA Settings in SmartPLS
Target Construct
Select a target construct for the importance-performance map analysis (IPMA).
IPMA Results
The following settings allow you to select between different importance-performance map representations for the selected target construct.
| Option | Constructs included |
|---|---|
| All predecessors of the selected target construct (including MV charts) | All constructs in the PLS path model that are indirect and direct predecessor constructs of the selected target construct. |
| Direct predecessors of the selected target construct (including MV charts) | Only the constructs in the PLS path model that are direct predecessor constructs of the selected target construct. |
Ranges
The IPMA rescales the data to provide performance scores on a scale from 0 to 100. For the correct rescaling, the original scales of the data are essential information. Here, you can check and correct the possible ranges of the manifest variables. For example, a 7-point Likert scale must have a minimum value of 1 and a maximum value of 7 in the IPMA settings.
The preconfigured ranges are based on the actual minimum and maximum values found in the dataset. This does not necessarily equal the theoretically possible range and should therefore be adjusted.
Again, the correct ranges are necessary to calculate correct performance values for the importance-performance map.
Frequently Asked Questions
What does IPMA add compared to standard PLS-SEM results?
Standard PLS-SEM results tell you how important a construct is for explaining a target construct, but not how well that construct currently performs. IPMA adds this performance dimension, letting you prioritize constructs that are highly important but currently underperforming.
How do I choose which predecessor constructs to include in the map?
You can choose between including all indirect and direct predecessor constructs of the selected target construct, or only its direct predecessor constructs, both including manifest variable (MV) charts.
Why do I need to set the possible ranges of the manifest variables?
IPMA rescales performance scores to a 0-100 scale, and this rescaling requires the correct theoretical range of each manifest variable (e.g., 1 to 7 for a 7-point Likert scale). The preconfigured ranges are based on the actual minimum and maximum values observed in the dataset, which do not necessarily match the theoretically possible range, so you should verify and correct them.
Which constructs should I prioritize for managerial action based on IPMA?
Constructs that show high importance for explaining the target construct but relatively low performance are the best candidates for improvement, since they offer the largest potential gain relative to the effort required.
Related SmartPLS Methods
- Necessary Condition Analysis (NCA)
- Finite Mixture Partial Least Squares (FIMIX-PLS)
- PLS Prediction-oriented Segmentation (PLS-POS)
- PLSpredict
References
- Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced issues in partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.
- Hauff, S., Richter, N. F., Sarstedt, M., & Ringle, C. M. (2024). Importance and performance in PLS-SEM and NCA: Introducing the combined importance-performance map analysis (cIPMA). Journal of Retailing and Consumer Services, 78, Article 103723.
- Höck, C., Ringle, C. M., & Sarstedt, M. (2010). Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. International Journal of Services Technology and Management, 14(2/3), 188–207.
- Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886.
- Rigdon, E. E., Ringle, C. M., Sarstedt, M., & Gudergan, S. P. (2011). Assessing heterogeneity in customer satisfaction studies: Across industry similarities and within industry differences. Advances in International Marketing, 22, 169–194.
- Sarstedt, M., Richter, N. F., Hauff, S., & Ringle, C. M. (2024). Combined importance-performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS-SEM): A SmartPLS 4 tutorial. Journal of Marketing Analytics, 12, 746–760.
- Schloderer, M. P., Sarstedt, M., & Ringle, C. M. (2014). The relevance of reputation in the nonprofit sector: The moderating effect of socio-demographic characteristics. International Journal of Nonprofit and Voluntary Sector Marketing, 19(2), 110–126.
- More literature ...
Cite correctly
Please always cite the use of SmartPLS!
Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2024). SmartPLS 4. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com
