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 findings 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 over-indebtedness on both facets of well-being (as well as on reported health and sleep). Financial well-being (a specific 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.

This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field 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 Nu-Support 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 findings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profiles of over-indebtedness.

This paper addresses whether overindebted and non-overindebted consumers differ in their attitude toward money (specifically, the degree to which consumers care about money and feel difficulties keeping track of their money) and how this attitude impacts three different financial behavior categories: record keeping (e.g., recording spending in writing), adjusting balance (e.g., trying to find ways to decrease one’s expenses to match income), and monitoring balance (e.g., monitoring one’s spending to see if it is in line with what is expected). Overindebted consumers were recruited via an NGO for consumer defense and were categorized (whenever possible) into two subgroups: consumers who became overindebted due to internal causes (e.g., bad financial management) and consumers who became overindebted due to external causes (e.g., unemployment). Non-overindebted consumers were a convenience sample. Non-overindebted consumers showed more positive attitudes toward money than both groups of overindebted consumers and overindebted due to external causes showed more positive attitudes than overindebted consumers due to internal causes. All groups share similar financial management behaviors except for monitoring balance, which was more frequent among non-overindebted consumers. Furthermore, a regression analysis indicates that money attitudes helped explain financial behavior differences between consumers above and beyond their indebtedness status. Consumers’ attitude predicted financial behaviors, even when controlling for relevant socioeconomic variables (education, income, age, and gender). Further analyses comparing money attitudes and financial behavior for the three subgroups (non-overindebted, overindebted due to internal causes, and overindebted due to external causes) showed no differences.

Three experiments were designed to test whether experimentally created ad hoc associative networks evoke false memories. We used the DRM (Deese, Roediger, McDermott) paradigm with lists of ad hoc categories composed of exemplars aggregated toward specific goals (e.g., going for a picnic) that do not share any consistent set of features. Experiment 1 revealed considerable levels of false recognitions of critical words from ad hoc categories. False recognitions occurred even when the lists were presented without an organizing theme (i.e., the category's label). Experiments 1 and 2 tested whether (a) the ease of identifying the categories' themes, and (b) the lists' backward associative strength could be driving the effect. List identifiability did not correlate with false recognition, and the effect remained even when backward associative strength was controlled for. Experiment 3 manipulated the distractor items in the recognition task to address the hypothesis that the salience of unrelated items could be facilitating the occurrence of the phenomenon. The effect remained when controlling for this source of facilitation. These results have implications for assumptions made by theories of false memories, namely the preexistence of associations in the activation-monitoring framework and the central role of gist extraction in fuzzy-trace theory, while providing evidence of the occurrence of false memories for more dynamic and context-dependent knowledge structures. (PsycINFO Database Record

Four studies explore semantic memory intrusions for goal-derived subcategories (e.g., "sports good for backache") embedded in taxonomic categories (e.g., "sports"). Study 1 presented hybrid lists (composed of typical items from both representations: taxonomic categories and subcategories) together with names of subcategories, names of taxonomic categories, or with no names. Subcategory names produced levels of false recognitions for critical lures from subcategories comparable with critical lures from taxonomic categories. Study 2 presented lists of exemplars either from taxonomic categories or subcategories (between participants). Lists of subcategories paired with their names produced higher levels of false recognition for subcategories lures compared with taxonomic lures. Study 3 replicated this result and showed that even though distinctiveness of taxonomic lures in a subcategory context (i.e., subcategory list with a subcategory name) may facilitate rejection of these lures, subcategory lures were still more falsely recognized than were taxonomic lures when retrieval monitoring was hindered through speeded recognition. Study 4 replicated the results with lists in which production frequency was better controlled and with a larger sample allowing for increased power of the test. Although confirming the critical role of preexistent categorical structures in the generation of false memories, results show that false memories for goal-derived subcategories can occur with the same frequency as false memories stemming from better established taxonomic categories. Such results broaden the scope of occurrence of false memories to goal-derived semantic organizations, which are often closer to categorizations used in real-world environments.