Talk up or criticize? Customer responses to WOM about competitors during social interactions

Popular metrics such as the Net Promoter Score (NPS) highlights many benefits of word of mouth (WOM) to firms. Is WOM all it is claimed to be? Building on social identity theory, this research develops a conceptual model of WOM exchange in social settings and tests the model with customer surveys of three service sectors. The findings show that the effects of (1) positive and negative WOM (P/NWOM) received about competitors and (2) perceived presence of critical incidents (PPCIs) on P/NWOM given about own service provider are far from intuitive. Responses to PWOM received counter the suggestions in the NPS literature. The findings also indicate that the best firms can hope for when receiving NWOM about competitors is that their customers remain silent. It is recommended that firms communicate a message that is consistent with the nuanced views expressed by friends in social circles, rather than a uniformly superior positioning

Population Structure and Economic Cycles in Greece. A Multidimensional Regional Analysis (1988-2016)

Demographic structures have undergone important transformations driven by economic cycles because of population movements and spatially-variable patterns of fertility and mortality. Understanding the latent relationship between changes over time in population structure and sequential waves of expansion and recession is a relevant issue in economic demography. In this regard, the recent history of southern European countries, and especially of Greece, is representative of consecutive economic expansions and recessions. The present study aims at investigating relevant modifications in population structure across Greek regions between 1988 and 2016 using a multi-temporal factor analysis. Being characterized by a relatively young population with traditional family structures, out-migration and moderate immigration up to the late 1980s, Greek demography shifted towards ageing, mononuclear families and a rising immigration rate during the early 2000s economic expansion, with an overall increase of resident population. The subsequent 2007 recession has represented a turning point in Greek demography, consolidating changes in traditional family structures, while stimulating outmigration to northern and western European countries and reducing immigration from developing countries. A diachronic analysis of population structures at sub-national scale indicates a substantial heterogeneity of demographic processes across Greek regions. Metropolitan areas and highly accessible coastal and flat districts including islands experienced rapid population dynamics, while peripheral rural regions underwent a moderate population ageing. Taken together, these processes had a short-term, synergic impact on Greek demographic structure determining a rapid increase in the median population age with possibly negative consequences for the ability of the country's economy to recover from crisis

Unveiling the consequences of prejudice against muslim women: How the hijab can influence islamophobia perception, acculturation preferences and well-being

In the interpretation of the Islamic culture, Muslim women have been placed, particularly, as one of the main objects of cultural differentiation. Restrictions on the use of clothing and religious symbols at work or in public spaces have continued to guide innumerous debates in the European Union (EU). The study in question investigated how islamophobia perception and different sub-dimensions of acculturation orientations (i.e., cultural maintenance, desire for contact) mediate the relationship between frequency of use of the hijab and well-being of Muslim immigrant women in Spain. Results indicated that the relationship between frequency of the use of hijab and well-being was mediated by islamophobia perception, suggesting that the more the participants wear the hijab, the more they perceived islamophobia and reported less well-being. Also, islamophobia perception was positively correlated to both the participant’s desire for contact and cultural maintenance. On the other hand, the participant’s acculturation preferences were not correlated to well-being.Nas interpretações da cultura islâmica, as mulheres muçulmanas são geralmente apresentadas como um dos principais casos de diferenciação cultural. Atualmente, inúmeros debates na União Européia (UE) têm feito referência à restrições ao uso de vestuários e símbolos religiosos no trabalho ou em espaços públicos, em especial à mulheres muçulmanas. O estudo em questão investigou como a percepção da islamofobia e diferentes sub-dimensões das orientações de aculturação (manutenção cultural, desejo de contato) mediam a relação entre a freqüência do uso do hijab e o bem-estar de mulheres imigrantes muçulmanas na Espanha. Os resultados indicaram que a relação entre a freqüência do uso do hijab e o bem-estar foi mediada pela percepção de islamofobia, sugerindo que quanto mais as participantes usavam o hijab, mais elas percebiam islamofobia e relatavam menos bem-estar. Somado a isso, a percepção de islamofobia foi positivamente correlacionada com as dimensões, tanto de desejo de contato como manutenção cultural. Por outro lado, as preferências de aculturação das participantes não apresentaram correlação com bem-estar

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein-Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters' emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach

An Object-Oriented Bayesian Framework for the Detection of Market Drivers

We use Object Oriented Bayesian Networks (OOBNs) to analyze complex ties in the equity market and to detect drivers for the Standard & Poor\u2019s 500 (S&P 500) index. To such aim, we consider a vast number of indicators drawn from various investment areas (Value, Growth, Sentiment, Momentum, and Technical Analysis), and, with the aid of OOBNs, we study the role they played along time in influencing the dynamics of the S&P 500. Our results highlight that the centrality of the indicators varies in time, and offer a starting point for further inquiries devoted to combine OOBNs with trading platforms

Technology Within Cultures: Segmenting the Wired Consumers in Canada, France, and the USA

This paper uses a state-of-the-art quantitative modeling approach to latent class analysis to analyze American, Canadian, and French consumers’ perception of technology-based products and their cultural values. It identifies hidden segments of consumers based on technology adoption propensity, cosmopolitan characteristics, and identification with the global consumer culture. The study emphasizes the diversity and variability between and among countries regarding localism, globalism, cosmopolitanism, and the global consumer culture. The framework provides a new way to evaluate modern consumers and reflects the combination of national/regional cultural characteristics and global culture elements while highlighting the relevance of modern technologies and communication methods in leveling consumer preferences and attitudes across cultures. From a theoretical viewpoint, this article provides a new framework incorporating technology adoption propensity and cultural elements in the empirical evaluation of modern consumers