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Perceived Causes and Attitudes Regarding Overindebtedness and Their Effects on Public Agreement With Government Financial Aid
Publication . Soro, Jerônimo C.; Ferreira, Mário B.; de Almeida, Filipa; Silva, Carla; Reis, Joana
In order to better understand how the problem of overindebtedness is perceived from
a laypeople standpoint, Study 1 inquired both overindebted and non-overindebted
consumers on the perceived causes of and attitudes toward the overindebted.
Situational and dispositional factors were perceived to have similar impact as causes of
overindebtedness, but non-overindebted consumers showed stronger agreement with
those causes than overindebted consumers. Regarding attitudes, non-overindebted
consumers tended to blame overindebted people for their situation rather than
perceiving them as victims, whereas overindebted consumers showed the opposite
pattern. Study 2 used a sample of (non-overindebted) consumers to assess the impact
of perceived causes of overindebtedness, attitudes toward the overindebted, and
political orientation on public support of government policies for aiding overindebted
people. We discuss the contributions of the present findings to design public policies
aimed at aiding overindebted households that are more aligned with the beliefs and
attitudes of the general public.
Using artificial intelligence to overcome over-indebtedness and fight poverty
Publication . Ferreira, Mário B.; Pinto, Diego; Herter, M. M.; C. Soro, Jerônimo; Vanneschi, Leonardo; Castelli, Mauro; Peres, Fernando
This research examines how artifcial intelligence may contribute to better understanding and to overcome overindebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a
feld database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors.
First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters:
low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). Second, supervised
machine learning with exhaustive grid search hyperparameters (32,730 predictive models) suggests that NuSupport Vector Machine had the best accuracy in predicting families’ over-indebtedness risk factors (89.5%).
By proposing an AutoML approach on over-indebtedness, our research adds both theoretically and methodologically to current models of scarcity with important practical implications for business research and society.
Our fndings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing
customized interventions for different profles of over-indebtedness.
On the Relation Between Over-Indebtedness and Well-Being: An Analysis of the Mechanisms Influencing Health, Sleep, Life Satisfaction, and Emotional Well-Being
Publication . Ferreira, Mário B.; de Almeida, Filipa; Soro, Jerônimo C.; Herter, M. M.; Pinto, Diego; Silva, Carla
This paper aims to explore the association between over-indebtedness and two facets
of well-being – life satisfaction and emotional well-being. Although prior research has
associated over-indebtedness with lower life satisfaction, this study contributes to the
extant literature by revealing its effects on emotional well-being, which is a crucial
component of well-being that has received less attention. Besides subjective well-being
(SWB), reported health, and sleep quality were also assessed. The fndings suggest that
over-indebted (compared to non-over-indebted) consumers have lower life satisfaction
and emotional well-being, as well as poorer (reported) health and sleep quality. Furthermore,
over-indebtedness impacts life satisfaction and emotional well-being through different
mechanisms. Consumers decreased perceived control accounts for the impact of overindebtedness on both facets of well-being (as well as on reported health and sleep).
Financial well-being (a specifc component of life satisfaction), partly mediates the impact
of indebtedness status on overall life satisfaction. The current study contributes to research
focusing on the relationship between indebtedness, well-being, health, and sleep quality,
and provides relevant theoretical and practical implications.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
PTDC/MHC-PAP/1556/2014