Non-symmetrical composite-based path modeling

Pasquale Dolce (), Vincenzo Esposito Vinzi and Natale Carlo Lauro
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Pasquale Dolce: University of Naples Federico II
Vincenzo Esposito Vinzi: ESSEC Business School of Paris
Natale Carlo Lauro: University of Naples Federico II

Advances in Data Analysis and Classification, 2018, vol. 12, issue 3, No 14, 759-784

Abstract: Abstract Partial least squares path modeling presents some inconsistencies in terms of coherence with the predictive directions specified in the inner model (i.e. the path directions), because the directions of the links in the inner model are not taken into account in the iterative algorithm. In fact, the procedure amplifies interdependence among blocks and fails to distinguish between dependent and explanatory blocks. The method proposed in this paper takes into account and respects the specified path directions, with the aim of improving the predictive ability of the model and to maintain the hypothesized theoretical inner model. To highlight its properties, the proposed method is compared to the classical PLS path modeling in terms of explained variability, predictive relevance and interpretation using artificial data through a real data application. A further development of the method allows to treat multi-dimensional blocks in composite-based path modeling.

Keywords: PLS path modeling; Non-symmetrical analysis; Predictive composite-based methods; 62H99 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11634-017-0302-1

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