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Reflective vs Formative Example: Computer Self-Efficacy Reflective – I am capable at performing tasks on my computer. I feel confident in my ability to perform computer-related tasks. Formative – I am confident at my ability to perform tasks in MS Word. I am skillful at using Excel. Example: System Quality Reflective – Overall, I would rate the system quality of the system highly. The quality of the system is appropriate for my needs. Formative – Reliability, Ease of Use, Complexity, Accessibility, Responsiveness
Partial least squares (PLS) was invented by Herman Wold (mentor to Karl Jöreskog)
as an analytical alternative for situations where theory is weak and where the available manifest variables or measures would likely not conform to a rigorously specified measurement model (soft modeling).
PLS method is designed to maximize prediction rather than fit.
to maximize the proportion of variance of the dependent "construct" that is explained by the predictor "constructs.“
Some researchers argue that the "latent constructs" in PLS are not really "latent" at all, since they are strict linear composites of observed variables.
Research hypotheses H1: ‘Information usefulness’ is positively related to ‘attitude towards IR Websites’ H2: ‘Usability’ is positively related to ‘attitude towards IR Websites’ H3: ‘Attractiveness’ is positively related to ‘attitude towards IR Websites’ H4: ‘Attitude towards IR Websites’ is positively related to ‘intention to re-use IR Website’
For constructs with reflective measures (i.e. latent constructs), one examines the loadings, which can be interpreted in the same manner as the loadings in a principal component analysis.
For constructs using formative measures (i.e. emergent constructs), the weights provide information as to what the makeup and relative importance are for each indicator in the creation/formation of the component.
Individual reflective item reliability is considered adequate when an item has a factor loading that is greater than 0.707 on its respective construct.
The internal consistency for a given block of indicators is assessed using the composite reliability.
Nunnally (1978) suggests 0.7 as a benchmark for a modest reliability applicable.
A model is also said to converge when Average variance extracted (AVE) (Fornell and Larcker, 1981) is greater than 0.50 meaning that 50 per cent or more variance of the indicators should be accounted for.
AVE assesses the amount of variance that a construct captures from its indicators relative to the amount due to measurement error.
Discriminant validity indicates the extent to which a given construct is different from other latent variables.
AVE should be greater than the variance shared between the latent construct and other latent constructs in the model (i.e. the squared correlation between two constructs) (Barclay et al. , 1995).