Quality & Quantity

Abstract This paper analyses the economic impact of tourism on the Italian economy through an integrated empirical application of Tourism Satellite Accounts (TSA) and Input–Output (IO) analysis. Rather than proposing a wholly new methodological framework, the study refines and applies established TSA-based IO integration techniques to the Italian case using official ISTAT data. Tourism is constru…

Abstract This study examines how elements of sustainable organizational culture in tourism enterprises influence consumers’ perceptions and destination-related decisions. Drawing on the Theory of Planned Behavior (TPB) and the Norm Activation Theory (NAT), the study explores the relationships between sustainability awareness, sustainability attitudes, the perceived importance of organizational cu…

Human beings and artificial intelligence models learn in fundamentally different ways. Humans update beliefs through repeated exposure, a Hebbian process that creates cognitive “inertia” causing beliefs to resist change. Large language models (LLMs), by contrast, do not update parameters at inference and are unburdened by cumulative experience. Despite growing interest in comparing human and mach…

Understanding the statistical interdependencies among Sustainable Development Goal (SDG) indicators poses significant methodological challenges, particularly in the presence of multicollinearity and limited cross-country observations. This study provides a comparative assessment of regression techniques for analysing income-related inequality (SDG indicator 10.1.1) in EU Member States, using sele…

Despite recent progress towards a more balanced work environment in terms of gender, the Gender Pay Gap is still a widespread relevant issue both from the economic and sociopolitical points of view. This paper focuses on Italy and uses AlmaLaurea survey data on University of Palermo graduates one year after graduation. The aim is to investigate the potential mediator role of the field of study (S…

This study proposes a machine learning framework to support risk‑based verification of expenditure declarations in European Structural and Investment Funds, reflecting the current regulatory emphasis on proportional and data‑driven audit strategies. Quantitatively, the problem is formulated as an imbalanced three-class classification task with ordered outcomes on high-dimensional administrative d…