The COVID-19 pandemic is far more than a health crisis: it has unpredictably changed our whole way of life. As suggested by the analysis of economic data on sales, this dramatic scenario has also heavily impacted individuals’ spending levels. To better understand these changes, the present study focused on consumer behavior and its psychological antecedents. Previous studies found that crises differently affect people’s willingness to buy necessities products (i.e., utilitarian shopping) and non-necessities products (i.e., hedonic shopping). Therefore, in examining whether changes in spending levels were associated with changes in consumer behavior, we adopted a fine-grained approach disentangling between necessities and non-necessities. We administered an online survey to 3833 participants (age range 18–64) during the first peak period of the contagion in Italy. Consumer behavior toward necessities was predicted by anxiety and COVID-related fear, whereas consumer behavior toward non-necessities was predicted by depression. Furthermore, consumer behavior toward necessities and non-necessities was predicted by personality traits, perceived economic stability, and self-justifications for purchasing. The present study extended our understanding of consumer behavior changes during the COVID-19 pandemic. Results could be helpful to develop marketing strategies that consider psychological factors to meet actual consumers’ needs and feelings.

(1) Background: Psychological well-being (PWB) is a multidimensional construct which is a key protective factor against chronic diseases in older adults. Numerous psychological and cognitive factors can influence older people’s PWB. However, while most studies have explored the effect of general cognition, only a few investigated the role of specific cognitive functions such as the efficiency of executive functions. Thus, the present study aimed to directly explore the relationships between PWB, age, and the core executive functions (namely working memory, flexibility, and inhibition). (2) Methods: A sample of 51 healthy older adults (age range: 60–88) were enrolled and tested both for PWB (by Ryff’s psychological well-being scale) and the core executive functions (by the Digit span backward, the Trail Making Test, and the Stroop test). (3) Results: Age and inhibitory skills are negatively correlated to the personal growth subscale of the PWB scale. Moreover, a hierarchical regression analysis showed that inhibition, but not working memory and flexibility, predicted the sense of continued growth and development (personal growth) of older adults, even after controlling for age and educational levels. Limitations and implications for future research and prevention interventions are discussed.

Previous studies consistently showed age-related differences in temporal judgment and temporal memory. Importantly, emotional valence plays a crucial role in older adults’ information processing. In this study, we examined the effects of emotions at the intersection between time and memory, analysing age-related differences in a temporal source memory task. Twenty-five younger adults (age range 18–35), 25 old adults (age range 65–74), and 25 old–old adults (age range 75–84) saw a series of emotional pictures in three sessions separated by a one-day rest period. In the fourth session, participants were asked to indicate in which session (1, 2, or 3) they saw each picture. Results showed that old–old adults tended to collocate negative pictures distant in time, while positive stimuli were remembered as more recent than real, compared to neutral pictures. To a lower extent, people over 65 showed the same pattern of results. In contrast, emotional valence did not affect younger adults’ temporal positioning of stimuli. Current findings fit well with the Socio-Emotional Selectivity Theory’s assumptions and extended the literature on the positivity effect to temporal source memory.

We investigated people’s preferences in COVID-19 vaccine allocation priority, comparing different social categories based on age and occupation. Vaccine allocation preferences were related to perce...

Background: Cognitive biases are popular topics in psychology and marketing, as they refer to systematic cognitive tendencies in human thinking that deviate from logical and rational reasoning. The framing effect (FE) and the decoy effect (DE) are examples of cognitive biases that can influence decision making and consumer preferences. The FE involves how options are presented, while the DE involves the addition of a third option that influences the choice between the other two options. Methods: We investigated the interaction between the FE and the DE in the case of both incongruent (ID) and congruent (CD) decoys in a sample of undergraduates (n = 471). The study had a two (positive vs. negative valence) × three (original, congruent decoy, incongruent decoy) within-subject design. Results: The ID option reduces the FE in both positive- and negative-framed conditions compared to the controls, while adding the CD option increases the FE only in the positive-framed condition. Additionally, the inclusion of the CD option enhances the level of decision confidence, whereas no significant differences were found in the ID condition. Conclusions: Our findings gave new insights into the interplay between two of the most frequent cognitive biases.

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.