In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

Assessing Household Food Waste in Italy: A Methodology for Detecting Drivers and Quantities

It has been estimated that one third of edible food destined for human consumption is lost or wasted along the food supply chain globally. Much of the waste comes from Global North, where consumers are considered as the bigger contributors. Different studies tried to analyze and estimate the Household Food Waste (HFW), especially in UK and Northern Europe. The result is that accurate studies at national level exist only in UK, Finland and Norway while no such studies are available in Italy, except for survey- based researches. Though, there is a widespread awareness that such methods might be not able to estimate Food Waste. Results emerging from literature clearly suggest that survey estimate inferior amounts of Food Waste as a result, if compared to waste sorting and weighting analysis or to diary studies. The hypothesis that household food waste is under-estimated when gathered through questionnaires has been enquired into. First, a literature review of behavioral economics and heuristics has been proposed; then, a literature review of the sector listing the existing methodologies to gather national data on Household Food Waste has been illustrated. Finally, a pilot experiment to test a mixed methodology is proposed. While literature suggests that four specific cognitive biases might be able to affect the reliability of answers in questionnaires, results of the present experiment clearly indicate that there is a relevant difference between how much the individual thinks to waste and he/she actually does. The result is a mixed methodology based on questionnaire, diary and waste sorting, able to overcome the cons of each single method.