Metacognition biases information seeking in assessing ambiguous news.

How do we assess the veracity of ambiguous news, and does metacognition guide our decisions to seek further information? In a controlled experiment, participants evaluated the veracity of ambiguous news and decided whether to seek extra information. Confidence in their veracity judgments did not predict accuracy, showing limited metacognitive ability when facing ambiguous news. Despite this, confidence in one's judgment was the primary driver of the demand for additional information about the news.

People devalue generative AI's competence but not its advice in addressing societal and personal challenges.

The release of ChatGPT and related tools have made generative artificial intelligence (AI) easily accessible for the broader public. We conducted four preregistered experimental studies (total N = 3308; participants from the US) to investigate people's perceptions of generative AI and the advice it generates on how to address societal and personal challenges. The results indicate that when individuals are (vs.

The role of Contact-Tracing Mobile Apps in pandemic prevention: A multidisciplinary perspective on health beliefs, social, and technological factors.

During the recent COVID-19 pandemic, governments implemented mobile applications for contact tracing as a rapid and effective solution to mitigate the spread of the virus. However, these seemingly straightforward solutions did not achieve their intended objectives. In line with previous research, this paper aims to investigate the factors that influence the acceptance and usage of contact-tracing mobile apps (CTMAs) in the context of disease control.

Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables.

The other side of the coin: An integrative review connecting pay and health.

The organizational sciences have long been interested in the effects of various compensation strategies, and on enhancing employee health. Research examining the connection between pay and health, however, remains a relative rarity. The work that has been done is scattered across disparate disciplines and lacks a unified framework for systematically exploring the effects of pay on health.

Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data.

Objective: This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data.

Methods: The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors.

Is boredom at work bad for your health? Examining the links between job boredom and autonomic nervous system dysfunction.

Job boredom refers to an unpleasant state of passiveness at work that has been found to negatively relate to self-reported health. To date, however, the relation between job boredom and physiological indicators of health has not been examined. The present study investigates whether job boredom relates to dysfunction in autonomic nervous system (ANS) activity as indicated by reduced heart rate variability (HRV) during night sleep.

Brokerage rents and intermediation networks.

This paper provides experimental evidence on the economic determinants of intermediation networks by considering two pricing rules-respectively, criticality and betweenness-and three group sizes of subjects-10, 50, and 100 subjects. We find that when brokerage benefits accrue only to traders who lie on all paths of intermediation, stable networks involve interconnected cycles, and trading path lengths grow while linking and payoff inequality remain modest as the number of traders grows. By contrast, when brokerage benefits are equally distributed among traders on the shortest paths, stable networks contain a few hubs that provide the vast majority of links, and trading path lengths remain unchanged while linking and payoff inequality explode as the number of traders grows.

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma.

Background: We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.

Methods: The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.

Risk profiling of food security impediments using decision maker's behavioural preference towards operational risk management.

For different decision makers, their approach towards management of operational risks differs due to difference in background and behavioural preferences. For such reasons, they attach different importance to various perspectives to manage operational risks. Present study conducts and analyses risk profiling of food security impediments, in which operational risk has come up as an important impediment.

Analyzing eutrophication and harmful algal bloom dynamics in a deep Mediterranean hypereutrophic reservoir.

Excessive point and non-point nutrient loadings accompanied with elevated temperatures have increased the prevalence of harmful algal bloom (HAB). HABs pose significant environmental and public health concerns, particularly for inland freshwater systems. In this study, the eutrophication and HAB dynamics in the Qaraoun Reservoir, a hypereutrophic deep monomictic reservoir suffering from poor water quality, were assessed.

The costs of insecurity: Pay volatility and health outcomes.

Every day, millions of individuals rely on fluctuating financial rewards in the form of customer tips, commissions, piece-rate, and performance-based pay. While these compensation systems are increasingly common, the volatility in pay that they create may harm employee health. Based on conservation of resource theory assumptions that money is a valued resource, I propose that volatility in pay represents with costs to health.

Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach.

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms.

A data-driven spatially-specific vaccine allocation framework for COVID-19.

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity.

ERPs responses to dominance features from human faces.

Social dominance is an important feature of social life. Dominance has been proposed to be one of two trait dimensions underpinning social judgments of human faces. Yet, the neural bases of the ability to identify different dominance levels in others based on intrinsically facial cues remains poorly understood.

International market exposure to sovereign ESG.

We quantify equity and bond market sensitivity to sovereign ESG scores and their variations which, theoretically, is equivalent to evaluating the demand for ESG at the global scale. We do so by estimating a longitudinal model, at the issue level, that captures exposures to sovereign ESG factors for both equity and fixed income indices. In spite of the surging interest in ESG investing, our results do not support a strong impact of ESG factors on the returns of international markets, implying that the demand for ESG at the country level is not a significant driver of prices.

Biopharmaceutical R&D outsourcing: Short-term gain for long-term pain?

Research and development (R&D) outsourcing offers some obvious productivity benefits (e.g., access to new technology, variabilised costs, risk sharing, etc.

Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes.

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms.

Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19.

In rapidly changing business conditions, it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events, such as COVID-19, that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations, these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain, Industry 4.

The prosociality of married people: Evidence from a large multinational sample.

Single people are more likely to die from COVID-19. Here we study whether this higher death rate could be partly explained by differences in compliance with protective health measures against COVID-19 between single and married people, and the drivers of this marital compliance gap. Data collected from 46,450 respondents in 67 countries reveal that married people are more likely to comply with protective measures than single people.

Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19.

The outbreak of the COVID-19 pandemic has significantly augmented the complexity of information, adding to the challenges that firms face in effectively processing and grasping accurate information. As a result, the production uncertainty of firms has been seriously intensified during the pandemic, disrupting the normal operation of firms and their supply chains. Digital technologies serve as salient tools that help firms to process and analyse information, consequently enhancing firm resilience in the face of supply chain disruptions.

Mass Diffusion of Modern Digital Technologies as the Main Driver of Change in Sports-Spectating Audiences.

The rapid uptake of digital technologies is constantly transforming the modern culture of sports spectating; however, relatively little is known about the impact of digitalization on the changing face of global sports-consuming audiences, particularly from a qualitative perspective. In this article, the relationship between modern mass digital technologies (i.e.

To Lead, or to Follow? How Self-Uncertainty and the Dark Triad of Personality Influence Leadership Motivation.

Under uncertainty, leaders who possess dark triad personality traits seem able to attain leadership positions. We draw on uncertainty-identity theory and dark triad research to explore the effect of self-uncertainty on leadership motivation. Uncertainty-identity theory predicts that people can reduce self-uncertainty by identifying with groups and following their leaders, which suggests that self-uncertainty reduces people's own leadership motivation.

What Makes You a Whistleblower? A Multi-Country Field Study on the Determinants of the Intention to Report Wrongdoing.

Unlabelled: Whistleblowers have significantly shaped the state of contemporary society; in this context, this research sheds light on a persistently neglected research area: what are the key determinants of whistleblowing within government agencies? Taking a unique methodological approach, we combine evidence from two pieces of fieldwork, conducted using both primary and secondary data from the US and Indonesia. In Study 1, we use a large-scale survey conducted by the US Merit Systems Protection Board (MSPB). Additional tests are conducted in Study 1, making comparisons between those who have and those who do not have whistleblowing experience.

Perturbation of Right Dorsolateral Prefrontal Cortex Makes Power Holders Less Resistant to Tempting Bribes.

Bribery is a common form of corruption that takes place when a briber suborns a power holder to achieve an advantageous outcome at the cost of moral transgression. Although bribery has been extensively investigated in the behavioral sciences, its underlying neurobiological basis remains poorly understood. Here, we employed transcranial direct-current stimulation (tDCS) in combination with a novel paradigm ( = 119 adults) to investigate whether disruption of right dorsolateral prefrontal cortex (rDLPFC) causally changed bribe-taking decisions of power holders.