High order PLS path modeling to evaluate well-being merging traditional and big data: A longitudinal study

We propose using high order partial least squares path modeling (PLS-PM) todefine a synthetic Italian well-being index merging traditional data,represented by the Quality of Life index proposed by “Il Sole 24 Ore”, andinformation provided by big data, represented by a Subjective Well-beingIndex (SWBI) performed extracting moods by Twitter. High order constructs,which allow to define a more abstract higher-level dimension and its moreconcrete lower-order sub-dimensions, have gained wide attention inapplications of PLS-PM, and many contributions in literature proposed theiruse to build composite indicators. The aim of the paper is to underline somecritical issues in the use of these models and to suggest the implementation ofa new spurious repeated indicator approach. Furthermore, following somerecommendations proposed on the use of PLS-PM in longitudinal studies, wecompare the situation in 2016 and 2017.