A data frame containing 34 variables with 569 observations.
An object of class
data.frame with 569 rows and 34 columns.
Simulated data with the same correlation matrix as the data studied by Yoo et al. (2000) .
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.
Henseler J (2017).
“Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling.”
Journal of Advertising, 46(1), 178--192.
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 .
#============================================================================ # 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)