Observing structural labour mismatches across Spain, Colombia, Italy and Hungary, Cristian Manuel Andrés Porras Calderón set out to rethink recruitment as a data-driven, sustainable and human-centred system. As an Economics student, he developed the Multicountry Predictive Labor Adjustment System (SPAL-M) — an integrated framework that combines labour economics modelling, inverted recruitment, experiential evaluation based on Kolb’s Learning Model, and environmental sustainability criteria
By proposing that companies travel to candidates rather than the reverse, and by aligning job matching with predictive analytics and individual learning styles, he challenges traditional recruitment logic. His innovative concept demonstrates how economic theory, green transition priorities and behavioural insight can converge to create a more equitable, efficient and sustainable labour matching ecosystem.
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