Yooetal2000 {cSEM}

Data: Yooetal2000

Description

A data frame containing 34 variables with 569 observations.

Usage

Yooetal2000

Format

An object of class data.frame with 569 rows and 34 columns.

Details

The data is simulated and has the identical correlation matrix as the data that was analysed by Yoo et al. (2000) to examine how five elements of the marketing mix, namely price, store image, distribution intensity, advertising spending, and price deals, are related to the so-called dimensions of brand equity, i.e., perceived brand quality, brand loyalty, and brand awareness/associations. It is also used in Henseler (2017) and Henseler (2021) for demonstration purposes, see the corresponding tutorial.

Source

Simulated data with the same correlation matrix as the data studied by Yoo et al. (2000).

References

Henseler J (2017). “Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling.” Journal of Advertising, 46(1), 178–192. doi:10.1080/00913367.2017.1281780.

Henseler J (2021). Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables. Guilford Press, New York.

Yoo B, Donthu N, Lee S (2000). “An Examination of Selected Marketing Mix Elements and Brand Equity.” Journal of the Academy of Marketing Science, 28(2), 195–211. doi:10.1177/0092070300282002.

Examples

#============================================================================
# Example is taken from Henseler (2021)
#============================================================================
model_HOC="
# Measurement models FOC
PR =~ PR1 + PR2 + PR3
IM =~ IM1 + IM2 + IM3
DI =~ DI1 + DI2 + DI3
AD =~ AD1 + AD2 + AD3
DL =~ DL1 + DL2 + DL3
AA =~ AA1 + AA2 + AA3 + AA4 + AA5 + AA6
LO =~ LO1 + LO3
QL =~ QL1 + QL2 + QL3 + QL4 + QL5 + QL6

# Composite model for SOC
BR <~ QL + LO + AA

# Structural model
BR~ PR + IM + DI + AD + DL 
"

out <- csem(.data = Yooetal2000, .model = model_HOC,
            .PLS_weight_scheme_inner = 'factorial',
            .tolerance = 1e-06)