% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/feature_cipma.R
\name{assess_ipma}
\alias{assess_ipma}
\title{Importance-Performance Map Analysis (IPMA) for PLS-SEM}
\usage{
assess_ipma(seminr_model, target, scale_min = 1, scale_max = 7, seed = 123)
}
\arguments{
\item{seminr_model}{An estimated SEMinR model from \code{estimate_pls()}.}

\item{target}{Name of the target (endogenous) construct for the IPMA.}

\item{scale_min}{Minimum of the measurement scale (default 1).}

\item{scale_max}{Maximum of the measurement scale (default 7).}

\item{seed}{Random seed for reproducibility (default 123).}
}
\value{
An object of class \code{cipma_analysis} (see
  \code{\link{assess_cipma}} for details). The \code{nca} element will
  be \code{NULL}.
}
\description{
\code{assess_ipma} conducts an Importance-Performance Map Analysis (IPMA)
for a given target construct. It computes each predecessor's importance
(unstandardized total effect on the target) and performance (rescaled
0--100 mean construct score), identifying constructs with high importance
but low performance as priority areas for improvement.
}
\details{
This is a convenience wrapper around \code{\link{assess_cipma}} with
\code{nca = FALSE}. Use \code{\link{assess_cipma}} instead if you also
want Necessary Condition Analysis (NCA) to produce a combined IPMA.
}
\examples{
library(seminr)
library(seminrExtras)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  composite("Loyalty",      multi_items("CUSL", 1:3))
)

mobi_sm <- relationships(
  paths(from = "Image",       to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation", to = c("Value", "Satisfaction")),
  paths(from = "Value",       to = "Satisfaction"),
  paths(from = "Satisfaction",to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                          measurement_model = mobi_mm,
                          structural_model  = mobi_sm)

\donttest{
ipma_result <- assess_ipma(mobi_pls,
                            target = "Loyalty",
                            scale_min = 1,
                            scale_max = 10)
print(ipma_result)
plot(ipma_result)
}

}
\references{
Ringle, C. M. & Sarstedt, M. (2016). Gain More Insight from Your PLS-SEM
Results: The Importance-Performance Map Analysis. Industrial Management &
Data Systems, 119(9), 1865-1886.
}
\seealso{
\code{\link{assess_cipma}} for cIPMA (IPMA + NCA)
}
