Nothing
\dontrun{
# Example from Henseler (2020) about importance performance matrix analysis
# using the SQ dataset
dat=cbind(SQ,SQ[,1:22],SQ[,1:22])
# Give these indicators names
colnames(dat)=c(colnames(SQ),paste0(colnames(SQ)[1:22],'rep'),paste0(colnames(SQ)[1:22],'reprep'))
# Specify and estimate second-order model using the repeated indicators approach
model<-'
A01 <~ a01
A02 <~ a02
A03 <~ a03
A04 <~ a04
A05 <~ a05
A06 <~ a06
A07 <~ a07
A08 <~ a08
A09 <~ a09
A10 <~ a10
A11 <~ a11
A12 <~ a12
A13 <~ a13
A14 <~ a14
A15 <~ a15
A16 <~ a16
A17 <~ a17
A18 <~ a18
A19 <~ a19
A20 <~ a20
A21 <~ a21
A22 <~ a22
Tangibles <~ a01rep+a02rep+a03rep+a04rep
Reliability<~ a05rep+a06rep+a07rep+a08rep+a09rep
Responsiveness <~ a10rep+a11rep+a12rep+a13rep
Assurance<~a14rep+a15rep+a16rep+a17rep
Empathy<~a18rep+a19rep+a20rep+a21rep+a22rep
Sat <~ sat
SerQ<~a01reprep+a02reprep+a03reprep+a04reprep+a05reprep+a06reprep+a07reprep+a08reprep+a09reprep+a10reprep+a11reprep+a12reprep+a13reprep+a14reprep+a15reprep+a16reprep+a17reprep+a18reprep+a19reprep+a20reprep+a21reprep+a22reprep
SerQ~Tangibles+Reliability+Responsiveness+Assurance+Empathy
Sat~SerQ
Tangibles~A01+A02+A03+A04
Reliability~A05+A06+A07+A08+A09
Responsiveness~A10+A11+A12+A13
Assurance~A14+A15+A16+A17
Empathy~A18+A19+A20+A21+A22
'
# Estimate the model
out <- csem(.data = dat,.model = model,
.PLS_weight_scheme_inner = 'factorial',
.tolerance = 1e-06)
# Apply doIPMA function to obtain the neccesary outcome to
# plot the importance performance matrix
outIPA <- doIPMA(out)
plot(x = outIPA,.dependent = 'Sat',.level = 'construct',
.attributes = c("Tangibles","Reliability","Responsiveness", "Assurance","Empathy"))
}
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