High correlation increases the standard error of the indicator weights, thereby triggering type II errors (i.e., false negatives). 5.2 Indicator CollinearityĬollinearity occurs when two or more indicators in a formative measurement model are highly correlated. ( 2022) suggest the correlation of the formatively measured construct with the reflectively measured item(s) should be 0.708 or higher, which implies that the construct explains (more than) 50% of the alternative measure’s variance. When the model is based on secondary data, a variable measuring a similar concept would be used (Houston, 2004). Cheah, Sarstedt, Ringle, Ramayah, and Ting ( 2018) show that a global single item, which captures the essence of the construct under consideration, is generally sufficient as an alternative measure – despite limitations with regard to criterion validity (Diamantopoulos, Sarstedt, Fuchs, Wilczynski, & Kaiser, 2012 Sarstedt, Diamantopoulos, Salzberger, & Baumgartner, 2016). To execute this procedure for determining convergent validity, researchers must plan ahead in the research design stage by including an alternative measure of the formatively measured construct in their questionnaire. Originally proposed by Chin ( 1998), the procedure is referred to as redundancy analysis. The following figure shows an example of a reflective-reflective second-order model of corporate reputation (i.e., REPU) with competence (i.e., COMP) and likeability (i.e., LIKE) as first-order constructs in SmartPLS.In formative measurement model evaluation, convergent validity refers to the degree to which the formatively specified construct correlates with an alternative reflectively measured variable(s) of the same concept. While these relationships are mapped as path coefficients in a PLS-SEM analysis, from a modeling perspective, they correspond to loadings (in case of reflective-reflective and formative-reflective HCMs) or weights (in case of reflective-formative or formative-formative HCMs) and need to be interpreted as such. Different from other constructs in the PLS path model, the assessment of the HOC is not concerned with the relationships between the HOC and its indicator variables but the relationships between the HOC and its LOCs. When analyzing the results of a HCM estimation, researchers not only need to carefully evaluate the measurement models of the LOCs but also the measurement model of the HOC.
HOW TO ASSES VIF SMARTPLS 2.0 FORMATIVE INDICATOR SOFTWARE
Researchers can use the SmartPLS software to model any of the four HCM types introduced in this chapter. When specifying and estimating HCMs in PLS-SEM, researchers need to consider further aspects, which relate to the number of indicators per LOC, the PLS-SEM algorithm weighting scheme, and the use of Mode A and B weighting. The repeated indicators approach, the total effects analysis of a collect-type HCM (i.e., the extended repeated indicators approach Becker et al., 2012), and embedded and disjoint the two-stage approaches (Sartstedt et al., 2019) allow modeling and estimating HCMs in PLS-SEM. Conversely, the HOC is formed by the LOCs in reflective-formative and formative-formative HCMs, which is similar to formative measurement models. Generally, the HOC of reflective-reflective and formative-reflective HCMs represents a more general construct that-similar to reflective measurement models-simultaneously explains all the underlying LOCs.
Each of these HCM types depicts the specific relationship between the HOC and the LOCs as well as the measurement model used to operationalize the constructs on the lower-order level: reflective-reflective, reflective-formative, formative-reflective, and formative-formative. On these grounds, researchers choose from four major HCM types.
The establishment of HCMs builds on carefully established theoretical/conceptual considerations.
HCMs enable reducing the number of structural model relationships, making the PLS path model more parsimonious, while increasing the bandwidth of content covered by the respective constructs. Brief DescriptionĪ HCM embraces a more general construct (i.e., the HOC), measured at a higher level of abstraction, while simultaneously including several subcomponents (i.e., the LOCs), which cover more concrete traits of this construct. An ensuing HCM would include a general satisfaction construct along with several subconstructs that capture different more concrete attributes of satisfaction, such as satisfaction with the price, satisfaction with the service quality, satisfaction with the personnel, and satisfaction with the servicescape. For example, satisfaction may be measured at two levels of abstraction.
Establishing higher-order models or hierarchical component models (HCMs), as they are usually referred to in the context of PLS-SEM, most often involve testing second-order models that contain two-layer structures of constructs.