Gain more insight from your PLS-SEM results. The importance-performance map analysis
| DOI | https://doi.org/10.1108/IMDS-10-2015-0449 |
| Date | 17 October 2016 |
| Published date | 17 October 2016 |
| Pages | 1865-1886 |
| Author | Christian M. Ringle,Marko Sarstedt |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Gain more insight from
your PLS-SEM results
The importance-performance map analysis
Christian M. Ringle
Institute for Human Resource Management and Organizations (HRMO),
Hamburg University of Technology Hamburg (TUHH),
Hamburg, Germany and
Newcastle Business School, University of Newcastle, Newcastle, Australia, and
Marko Sarstedt
Faculty of Economics, Otto-von-Guericke University, Magdeburg, Germany and
Newcastle Business School, University of Newcastle, Newcastle, Australia
Abstract
Purpose –The purpose of this paper is to introduce the importance-performance map analysis (IPMA)
and explain how to use it in the context of partial least squares structural equation modeling (PLS-
SEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates
the results generation and interpretation.
Design/methodology/approach –The explications first address the principles of the IPMA and
introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a
case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies.
Findings –The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and,
thereby, gain additional results and findings. More specifically, instead of only analyzing the path
coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent
variables and their indicators (i.e. performance dimension).
Research limitations/implications –An IPMA is tied to certain requirements, which relate to the
measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes
linear relationships. This research does not address the computation and interpretation of non-linear
dependencies.
Practical implications –The IPMA is particularly useful for generating additional findings and
conclusions by combining the analysis of the importance and performance dimensions in practical
PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain
target construct. Expanding the analysis to the indicator level facilitates identifying the most
important areas of specific actions. These results are, for example, particularly important in practical
studies identifying the differing impacts that certain construct dimensions have on phenomena such as
technology acceptance, corporate reputation, or customer satisfaction.
Originality/value –This paper is the first to offer researchers a tutorial and annotated example of an
IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this
paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis,
using the SmartPLS 3 software.
Keywords Structural equation modeling (SEM), Partial least squares (PLS),
Unified theory of acceptance and use of technology (UTAUT), SmartPLS,
Importance-performance map analysis (IPMA)
Paper type General review
Industrial Management & Data
Systems
Vol. 116 No. 9, 2016
pp. 1865-1886
©Emerald Group Publis hing Limited
0263-5577
DOI 10.1108/IMDS-10-2015-0449
Received 31 October 2015
Revised 6 January 2016
4February2016
Accepted 14 February 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The authors thank Geoffrey S. Hubona for sending and granting the authors permission to use
the data of the study by Al-Gahtani et al. (2007). This paper uses the statistical software
SmartPLS 3 (www.smartpls.com). Ringle acknowledges a financial interest in SmartPLS.
1865
Importance-
performance
map analysis
Introduction
Partial least squares structural equation modeling (PLS-SEM; Chin, 1998; Garson, 2014;
Hair et al., 2017; Lohmöller, 1989; Rigdon, 2013; Tenenhaus et al., 2005; Wold, 1982) is a
variance-based method to estimate path models with latent variables. Th e PLS-SEM
approach is particularly useful when the study’s focus is on the analysis of a certain
target construct’s key sources of explanation. For example, the technology acceptance
model (TAM; Davis, 1989; Davis et al., 1989) and its various extensions, such as the
unified theory of acceptance and use of technology (UTAUT; Venkatesh et al., 2003),
are popular models for PLS-SEM applications in management information systems
research. In the marketing field, the American Customer Satisfaction Index (ACSI)
model (Anderson and Fornell, 2000; Fornell et al., 1996) is another widespread PLS-SEM
application. PLS-SEM enjoys rapidly increasing usage in various business disciplines,
such as accounting (Lee et al., 2011), family business (Sarstedt et al., 2014), international
business (Richter et al., 2015), management information systems (Ringle et al., 2012),
marketing (Hair et al., 2012), operations management (Peng and Lai, 2012), strategic
management (Hair et al., 2012a), and tourism research (do Valle and Assaker, 2015).
The purpose of this paper is to explain and illustrate the use of the
importance-performance map analysis (IPMA; also called importance-performance
matrix, impact-performance map, or priority map analysis), a useful analysis approach
in PLS-SEM that extends the standard results reporting of path coefficient estimates by
adding a dimension that considers the average values of the latent variable scores.
More precisely, the 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 (Fornell et al., 1996; Martilla and Jame s,
1977; Slack, 1994). The goal is to identify predecessors that have a relatively high
importance for the target construct (i.e. those that have a strong total effect), but also
have a relatively low performance (i.e. low average latent variable scores).
Illustrative example
To illustrate the concept of an IPMA, consider the PLS path model in Figure 1 with four
constructs Y
1
-Y
4
. In this PLS path model, Y
4
represents the final target variable,
X11
0.50
0.25
0.25
Y1
(53.7)
Y2
(85.6)
Y3
(61.8)
Y4
(78.1)
X12
X13
X14
X31
X21
X24
X23
X22
X41
X42
X43
X44
X32
X33
X34
0.25
0.50
0.50
Figure 1.
IPMA model
1866
IMDS
116,9
To continue reading
Request your trial