2017
DOI: 10.1007/s00187-017-0249-6
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The case of partial least squares (PLS) path modeling in managerial accounting research

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Cited by 121 publications
(67 citation statements)
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“…In this article, we used a partial least square (PLS-SEM) composite scheme to represent the total variance of the variables [66] for different reasons. In particular, PLS-SEM was an adequate model in the case of inclusion of archival information or secondary data [67].…”
Section: Data and Samplementioning
confidence: 99%
“…In this article, we used a partial least square (PLS-SEM) composite scheme to represent the total variance of the variables [66] for different reasons. In particular, PLS-SEM was an adequate model in the case of inclusion of archival information or secondary data [67].…”
Section: Data and Samplementioning
confidence: 99%
“…Once we reached this point, and because the characteristics of our variables were continuous and secondary, the use of Partial Least-Squares (PLS) was considered appropriate [76][77][78][79][80].…”
Section: Empirical Frameworkmentioning
confidence: 99%
“…Lastly, SmartPLS was used (version 3.2.8) to predict latent variables based on the estimation of ordinary least-squares and principal component analyses. Thus, a causal-predictive analysis in complex situations was carried out, but only after obtaining theoretical information, such as in our case [75][76][77][78][79][80][81][82][83][84].…”
Section: Empirical Frameworkmentioning
confidence: 99%
“…We then generate the data of the climate variables of our interest. Moreover, the Ghana malaria incidence data is sufficient for the application of PLS-PM due to its suitability for handling small sample data, non-normality, multi-dimensions and multicollinearity [24,25]. However, the sample set is not sufficient to obtain high precision accuracy when applying machine learning algorithms.…”
Section: Data Collection and Sourcementioning
confidence: 99%
“…The technique called PLS-PM or PLS-SEM was developed by [31] and chosen due to its characteristics in terms of small sample size, non-normality, multi-dimensions, and multicollinearity [23,24]. We have identified three hidden factors using EFA, and subsequently applied SEM for construction of the model (see Figure 3b).…”
Section: Estimation Of Pls-pmmentioning
confidence: 99%