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Next, we included the marker items as additional latent variables in our PLS analysis model and compared the results between the original research model (without the marker variables) and the common method bias test model (with marker variables).
Figure 11 Component planes for supervised SOMs for case study 3, illustrating marker variables for donor J and for treatment.
Recommendations for further research include the need for larger samples and the inclusion of specific marker variables.
The results indicate that the marker variables had no significant effects on the dependent variables (satisfaction and return intention) or on other effective endogenous variables (utilitarian utility, hedonic utility, and pleasure) (see Online Resource 1.1).
One of the most important uses of supervised SOMs involves determining what variables are important [55, 56] for the purpose of defining a class or group of samples, often called marker variables.
Although these results support the FTMT as a measure of nonverbal executive functioning, it would be helpful to conduct an additional factor analysis including additional measures of right frontal functioning, and perhaps other measures of right hemisphere functioning as marker variables.
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We assessed the correlation between a theoretically unrelated construct (marker variable) and the other constructs.
In addition to the Harman's single factor approach we also performed Lindell and Whitney's [81] marker variable test.
The results from the model indicate that the marker variable did not have any significant influence on the endogenous latent variables.
Thus, we adopted the marker variable approach (Rönkkö and Ylitalo 2011) to test whether a common method bias confounded our results.
Since the data were collected from single informants we assessed common method bias using Harman's [77] single factor test and a modified marker variable test [78-80] [78-80]
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