Through a Glass Darkly

Huysman, M.H. [Promotor]Verlegh, P.W.J. [Promotor]Verhagen, T. [Copromotor

“Fruit fight”: Schadenfreude and “Word-of-Mouth” among consumer groups

The present study investigates when young BlackBerry-users experience schadenfreude and engage in Word-of-Mouth after reading negative news about Apple’s iPhone. The results show that social identification with other BlackBerry-users and domain relevance interactively predict schadenfreude; the more important owning a smartphone is for an individual, the more identification with other BlackBerry-users increases the level of schadenfreude towards iPhone-users. Furthermore, hostile feelings towards the rival consumer group elicited higher levels of schadenfreude, which, in turn, led to a stronger intention to engage in negative Word-of-Mouth. These findings suggest that schadenfreude plays an important role in consumer behavior

“Fruit fight”: Leedvermaak en “Word-of-Mouth” bij consumentengroepen

Een onderzoek onder jonge BlackBerry-gebruikers toont aan dat domeinrelevantie en sociale identificatie met de eigen consumentengroep een interactieve bijdrage leveren aan de voorspelling van de mate van leedvermaak die zij ervaren om gebruikers van Apple’s iPhone na negatieve berichtgeving over dit product.Hoe belangrijker het hebben van een smartphone wordt gevonden, hoe meer identificatie met andere BlackBerry-gebruikers leedvermaak om iPhone-gebruikers versterkt. Bovendien resulteren vijandige gevoelens jegens iPhone-gebruikers tot meer leedvermaak, hetgeen vervolgens leidt tot een sterkere neiging om de negatieve berichten door te vertellen aan anderen (“word-of-mouth”). Dit suggereert dat leedvermaak een belangrijke rol speelt bij consumentengroepen

When we enjoy bad news about other groups: A social identity approach to out-group schadenfreude

Two studies investigate schadenfreude (pleasure at the misfortune of others) as an emotional response to news about out-group misfortunes in a political and consumer context by analyzing reactions of voters for opposition parties to the downfall of a Dutch coalition government (Study 1), and of BlackBerry-users to negative news reports about Apple’s iPhone (Study 2). Consistent with social identity theory and intergroup emotion theory, both studies demonstrate that affective in-group identification increases schadenfreude reactions to news about an out-group misfortune, provided that this misfortune occurs in a domain of interest to news recipients. Additional findings show that this interaction effect attenuates when a misfortune instead befalls the in-group (Study 1) and is still observed when controlling for affective dispositions towards the out-group (Study 2). Moreover, results suggest that schadenfreude reactions strengthen subsequent intentions to share news about the out-group’s misfortune with others or to engage in negative word-of-mouth (Study 2)

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024