Journal article Open Access

Partial least squares path modeling: Time for some serious second thoughts

Rönkkö, Mikko; McIntosh, Cameron N.; Antonakis, John; Edwards, Jeffrey R.

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      <creatorName>Rönkkö, Mikko</creatorName>
      <creatorName>McIntosh, Cameron N.</creatorName>
      <givenName>Cameron N.</givenName>
      <creatorName>Antonakis, John</creatorName>
      <creatorName>Edwards, Jeffrey R.</creatorName>
      <givenName>Jeffrey R.</givenName>
    <title>Partial least squares path modeling: Time for some serious second thoughts</title>
    <date dateType="Issued">2016-11-01</date>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.jom.2016.05.002</relatedIdentifier>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">Partial least squares (PLS) path modeling is increasingly being promoted as a technique of choice for various analysis scenarios, despite the serious shortcomings of the method. The current lack of methodological justification for PLS prompted the editors of this journal to declare that research using this technique is likely to be deck-rejected (Guide &amp;amp; Ketokivi, 2015).To provide clarification on the inappropriateness of PLS for applied research, we provide a non-technical review and empirical demonstration of its inherent, intractable problems. We show that although the PLS technique is promoted as a structural equation modeling (SEM) technique, it is simply regression with scale scores and thus has very limited capabilities to handle the wide array of problems for which applied researchers use SEM. To that end, we explain why the use of PLS weights and many rules of thumb that are commonly employed with PLS are unjustifiable, followed by addressing why the touted advantages of the method are simply untenable.</description>
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