Abstract

Standard PLS-SEM analyses provide information on the relative importance of constructs in explaining other constructs in the structural model. Information on the importance of constructs is relevant for drawing conclusions. The importance-performance map analysis (IPMA) extends the results of PLS-SEM by also taking the performance of each construct into account.

Description

Standard PLS-SEM analyses provide information on the relative importance of constructs in explaining other constructs in the structural model. Information on the importance of constructs is relevant for drawing conclusions. The importance-performance map analysis (IPMA) extends the results of PLS-SEM by also taking the performance of each construct into account. As a result, conclusions can be drawn on two dimensions (i.e., both importance and performance), which is particularly important in order to prioritize managerial actions. Consequently, 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.

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 the user to select between different importance-performance map representations for the selected target construct.

(1) All Predecessors of the Selected Target Construct (Including MV Charts)

The importance-performance map includes all constructs in the PLS path model that are indirect and direct predecessor constructs of the selected target construct in the PLS path model.

(2) Direct Predecessors of the Selected Target Construct (Including MV Charts)

The importance-performance map includes all constructs in the PLS path model that are only direct predecessor constructs of the selected target construct in the PLS path model.

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 data are essential information. Here, the user can check and correct the possible ranges of the manifest variables. For example a 7-point Likert sale 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 must not equal the theoretically possible ranges and should therefore be adjusted.

Again, the correct ranges are necessary to calculate correct performance values for the importance-performance map.

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., Thousand Oaks, CA: Sage.

• Höck, C., Ringle, C. M., and 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. and 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., and Gudergan, S. P. (2011). Assessing Heterogeneity in Customer Satisfaction Studies: Across Industry Similarities and Within Industry Differences, Advances in International Marketing, 22: 169-194.

• Schloderer, M. P., Sarstedt, M., and 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.

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