Negative Information Research Articles

Healthcare Workers’ Readiness for COVID-19 Vaccination Using Health Belief Model

Objective:: Acceptance of COVID-19 vaccination of medical personnel reduces the spread of infection the research aimed to evaluate healthcare professionals' attitudes toward vaccination by the health belief model. Methods:: A quantitative, cross-sectional research design was used for the study. Results:: Most medical personnel agreed to get the COVID-19 vaccination (84%; n=384). Medical workers over 45 (90%) showed more readiness for vaccination. The main pushbacks against the vaccination included the development of a vaccine in a short period (n=56; 73.7%), fear of side effects (n=49; 64.5%), lack of reliable information about vaccination (n=35; 46.1%), negative information spread by the media (n=26, 34.2%). Recommendations by healthcare workers (63.9%), health authorities (73.1), media (70%), large number of people being vaccinated (72.6%), and provision of trustworthy information (71.3%) facilitate vaccination. Conclusion:: The high rate of COVID-19 vaccination among medical personnel shows that they have a high risk perception of infection. Among Health Belief Model components, cues to actions were most important in willingness to carry out vaccination. Recommendations of the WHO and scientists significantly influence the willingness to carry out vaccination. The regulatory responsibilities of the state and the public's trust in it play a crucial role in raising the population's awareness about the vaccine.

Chinese young consumers’ response to negative brand information of electric vehicles – do personal cultural values matter?

Purpose This study aims to investigate the responses of young Chinese consumers with different cultural characteristics to negative brand information about electric vehicles. Design/methodology/approach The current study is quantitative research with an experimental method. It shows two different levels of severity for negative publicity and asks participants to self-report through questionnaires. Findings Chinese young consumers, being collectivist and of high uncertainty avoidance, tend to search for and spread information; consumers with low power distance search and share information more under low information severity. In addition, information search positively affects brand attitude under lower severity; negative word-of-mouth intention negatively affects brand attitudes at both severity levels. Research limitations/implications The current study examines the influence of personal cultural values on information searching and negative information dissemination among young consumers, providing insights to complement previous studies. Furthermore, it explores how such exposure influences young consumers’ brand attitude and intention to purchase. Limitations include simple sample scopes and single-product stimuli. Practical implications This research highlights the importance of cultural dimensions in shaping young consumers’ responses to negative publicity. Marketers worldwide should consider cultural influence and develop specific strategies to address negative information about different products. Understanding customers’ unique characteristics and preferences can help marketers effectively tailor their approaches to counter negative publicity. Originality/value This study originally provides a supplement to prior studies on cultural dimensions and consumer behavior and provides suggestions to marketers on young Chinese consumers.

Attentional biases to signals of negative information: Reliable measurement across three anxiety domains.

Cognitive models propose that individuals with elevated vulnerability to experiencing negative emotion are characterised by biased attentional responding to negative information. Typically, methods of examining these biases have measured attention to pictures of emotional scenes, emotional faces, or rewarding or feared objects. Though these approaches have repeatedly yielded evidence of anxiety-linked biases, their measurement reliability is suggested to be poor. Recent research has shown that attentional responding to cues signalling negative information can be measured with greater reliability. However, whether such biases are associated with emotion vulnerability remains to be demonstrated. The present study conducted three experiments that recruited participants who varied in trait and state anxiety (N = 134), social anxiety (N = 122), or spider fear (N = 131) to complete an assessment of selective attention to cues signalling emotionally congruent negative information. Analyses demonstrated that anxiety and fear were associated with biased attentional responding to cues signalling negative information, and that such biases could be measured with acceptable reliability (rsplit-half = .69-.81). Implications for research on the relation between emotion and attention are discussed.

Reducing the negative impact of ultra-processed foods: Consumer response to natural claims, organic claims, and processing level disclosures

Drawing from theories related to choice architecture, nudges, and the integration of negative and positive information, this research examines the use of ultra-processing package disclosures in conjunction with favorable food processing claims (e.g., “natural,” “organic”) and nutrient content (e.g., sugar) warnings on perceived food processing level, disease risk evaluations, and purchase intentions. Four studies examine how marketers’ positive claims and disclosures interact to jointly affect consumer evaluations of these outcomes. Results show the usefulness of stop-sign disclosures for ultra-processed foods in counterbalancing the positive inferences created from both unregulated (e.g., natural) and regulated (e.g., organic) package claims on perceived food processing level, which mediates the interaction effects on perceptions of disease risk, healthfulness, and purchase intentions. Because high ultra-processed food consumption has recently been linked to increased obesity, type-2 diabetes, heart disease, and cancer, there are implications of results for CPG marketers, consumers, and the global health community, as well as enhancing the conceptual understanding of efforts to promote consumer well-being.

Performance management and political accountability: how local governments respond to performance feedback

ABSTRACT Using the results of an annual assessment of local governments’ integrity levels from 2012 to 2016, we explore how Korea’s local governments respond to performance feedback provided by the central government. First, we find clear performance improvement only among localities performing below the average of their social comparison groups. This result supports the negativity bias hypothesis, which posits that policymakers are driven more by negative than by positive information. Second, local governments’ responses to feedback increased as election dates approached. This outcome suggests that electoral incentives magnify the effectiveness of performance management. Third, negative performance information released immediately before elections is associated with the incumbents’ electoral defeat, which is consistent with the hypothesis that performance information helps citizens hold policymakers accountable during elections. Overall, the findings imply that performance management (managerial accountability) and elections (political accountability) are mutually reinforcing mechanisms and that voter myopia can partially erode this synergy.

Hierarchical Convolutional Neural Network with Knowledge Complementation for Long-Tailed Classification

Existing methods based on transfer learning leverage auxiliary information to help tail generalization and improve the performance of the tail classes. However, they cannot fully exploit the relationships between auxiliary information and tail classes and bring irrelevant knowledge to the tail classes. To solve this problem, we propose a hierarchical CNN with knowledge complementation, which regards hierarchical relationships as auxiliary information and transfers relevant knowledge to tail classes. First, we integrate semantics and clustering relationships as hierarchical knowledge into the CNN to guide feature learning. Then, we design a complementary strategy to jointly exploit the two types of knowledge, where semantic knowledge acts as a prior dependence and clustering knowledge reduces the negative information caused by excessive semantic dependence (i.e., semantic gaps). In this way, the CNN facilitates the utilization of the two complementary hierarchical relationships and transfers useful knowledge to tail data to improve long-tailed classification accuracy. Experimental results on public benchmarks show that the proposed model outperforms existing methods. In particular, our model improves accuracy by 3.46% compared with the second-best method on the long-tailed tieredImageNet dataset.

A multiple risk coupled propagation model for emergency information considering government information and government mandatory measures

In the era of new media, it is crucial to investigate the transmission paths of emergency information due to the rising risk of such information. We take the risks caused by the dissemination of various heterogeneous negative information public opinion arising from the emergencies as the research object. Starting from the coupling of multiple risks and considering the behavioral impact of different government measures, we construct a three-layer coupling network S1I1S1-S2I2S2-UAU model between different types of negative information (Info1, Info2) and government information (Info3) diffusion, and analyze the coupling mechanisms of negative information and government information. Second, the MMCA is used to analyze the dissemination process and the diffusion threshold of government information. Finally, the rationality and effectiveness of the S1I1S1-S2I2S2-UAU model are verified by numerical simulation and real case. Our research results can help establish the cognitive of information risk coupling propagation law, providing support for government departments in controlling information risk.

Motivated Belief Updating and Rationalization of Information

We study belief updating about relative performance in an ego-relevant task. Manipulating the perceived ego relevance of the task, we show that subjects substantially overweight positive information relative to negative information because they derive direct utility from holding positive beliefs. This finding provides a behavioral explanation why and how overconfidence can evolve in the presence of objective information. Moreover, we document that subjects who receive more negative information downplay the ego relevance of the task. These findings suggest that subjects use two alternative strategies to protect their ego when presented with objective information. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: The authors gratefully acknowledge financial support from the ExperimenTUM. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02537 .

Semi-supervised nonnegative matrix factorization with label propagation and constraint propagation

Semi-supervised nonnegative matrix factorization (SNMF) improves the clustering effect of nonnegative matrix factorization (NMF) by integrating label information. However, as one of the most commonly used semi-supervised learning methods, label propagation algorithm (LP) has some limitations due to its heavy dependence on the predefined similarity matrix. To address this deficiency and effectively use limited supervisory information, a structural NMF algorithm with label propagation and constraint propagation (PNLP-SCNMF) is proposed in this paper, which simultaneously performs data representation and classification in a joint manner. Both positive label information and negative label information are introduced into the proposed method, and the factor matrix is constrained by label information to construct the tri-decomposition form. Constraint propagation algorithm (CPA) is used to correct the constructed similarity matrix, so that the low-dimensional subspace obtained by matrix decomposition can retain the geometric structure characteristics of the original sample space. In addition, an efficient iterative update optimization scheme is proposed to solve this objective function, and its convergence is proved theoretically. A large number of comparative experiments on nine real datasets show that the proposed method can consistently achieve higher clustering accuracy in image clustering tasks.

Fitting Insurance Claim Reserves with Two-Way ANOVA and Intuitionistic Fuzzy Regression

A highly relevant topic in the actuarial literature is so-called “claim reserving” or “loss reserving”, which involves estimating reserves to be provisioned for pending claims, as they can be deferred over various periods. This explains the proliferation of methods that aim to estimate these reserves and their variability. Regression methods are widely used in this setting. If we model error terms as random variables, the variability of provisions can consequently be modelled stochastically. The use of fuzzy regression methods also allows modelling uncertainty for reserve values using tools from the theory of fuzzy subsets. This study follows this second approach and proposes projecting claim reserves using a generalization of fuzzy numbers (FNs), so-called intuitionistic fuzzy numbers (IFNs), through the use of intuitionistic fuzzy regression. While FNs allow epistemic uncertainty to be considered in variable estimation, IFNs add bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis is grounded in the ANOVA two-way framework, which is adapted to the use of intuitionistic regression. Similarly, we compare our results with those obtained using deterministic and stochastic chain-ladder methods and those obtained using two-way statistical ANOVA.

Bridging the Gap Between Legitimacy and Neutralisation Strategies in Sustainability Reporting

Amid global sustainability challenges, such as climate change, companies operating in different industries face increasing demands for accountability and transparency. To meet these demands, sustainability reporting (SR) has become a mainstream business practice in communicating companies’ positive and negative sustainability impacts. This short paper aims to increase our understanding of how companies may release negative information while seeking to maintain their legitimacy. For this purpose, the paper bridges the gap between legitimacy strategies, impression management and neutralisation techniques by reviewing their associated literature. The nature of neutralisation techniques and how organisations use them in the SR context is underexplored by scholars. Finally, the paper presents prospective discussions of an ongoing study that examines negative sustainability disclosure in the tourism transportation context, namely the airlines.

Biodiversity management and stock price crash risk

AbstractThis study explores the link between corporate biodiversity management and the risk of sudden declines in future stock prices, an area largely overlooked in empirical research despite the societal significance of biodiversity loss. We posit that robust corporate biodiversity efforts mitigate the suppression of negative information, consequently reducing the risk of abrupt stock price declines. Leveraging a global dataset and innovative biodiversity management metrics from Moody's ESG Solutions (former Vigero Eiris), our multivariate regression analysis demonstrates that companies with stronger biodiversity structures and actions exhibit lower stock price crash risk. In an additional analysis, we focus on environmental inspections as a possible conduit for releasing negative information on biodiversity management. Using a subsample of North American firms that were inspected by the U.S. Environmental Protection Agency (EPA), we find that firms which experience an inspection record an increase in their stock price crash risk.

The Formal Analysis on Negative Information Selections for Privacy Protection in Data Publishing

Negative information selection is an approach to protect the privacy by using negative information to replace original information. In this paper, we prove some bounds for negative information selection. Those bounds reveal the privacy protection strength of quantitative probability analysis. We also analyzed the reconstruction probability of original information from available negative information. The formal analysis can specify the bound on the strength of security and utility for negative information selection. Besides, we simulate brute force attacks under different data leakage ratios. Specifically, we calculate the attacker’s guess times before and after the data leakage. Experimental results indicate that the data leakage of over 30% can put the original information in a dangerous situation. Furthermore, we found that the leakage possibility has little relevance to the number of elements in the full set, but it is influenced by the ratio of the leaked information.

코로나19에서의 부정적 감정, 정보 불충분성, 정보 추구로의 경로 이해 : 정치 성향·신뢰하는 정보 경로의 다집단 분석을 중심으로

코로나19에서의 부정적 감정, 정보 불충분성, 정보 추구로의 경로 이해 : 정치 성향·신뢰하는 정보 경로의 다집단 분석을 중심으로

Similarity measure between generalized intuitionistic fuzzy sets with three kinds of negation

 The similarity measure of intuitionistic fuzzy sets is a primary method for dealing with uncertainty and fuzzy problems and is commonly used in fuzzy decision-making and pattern recognition. The current mainstream similarity measure is based on the classical fuzzy set with only one negation, which often violates the intuitionistic problem in applications because the actual semantics of multiple negations are not considered. To solve the inconsistency and irrationality problems in the classical similarity methods, we introduce three negations (contradiction negation, opposition negation, and mediation negation) in the intuitionistic fuzzy set to obtain the generalized intuitionistic fuzzy set and prove its related property theorem. On this basis, our similarity measure adopts a mediational negation to represent non-membership, which fully utilizes the multiple negation information of non-membership and hesitancy and avoids the loss of fuzzy information. We verify the method’s rationality, validity, and originality through pattern recognition experiments and numerical examples, which improves the performance of intuitionistic fuzzy set similarity in practical applications and provides a new approach for future research on intuitionistic fuzzy inference.

Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based convolutional neural network framework with adaptively parametric ReLU (APtrans-CNN) for ECG signal denoising. The proposed APtrans-CNN architecture combines the strengths of transformers in global feature learning and CNNs in local feature learning to address the inadequacy of learning with long sequence time-series features. By fully exploiting the global features of ECG signals, our framework can effectively extract critical information that is necessary for signal denoising. We also introduce an adaptively parametric ReLU that can assign a value to the negative information contained in the ECG signal, thereby overcoming the limitation of ReLU to retain negative information. Additionally, we introduce a dynamic feature aggregation module that enables automatic learning and retention of valuable features while discarding useless noise information. Results obtained from two datasets demonstrate that our proposed APtrans-CNN can accurately extract pure ECG signals from noisy datasets and is adaptable to various applications. Specifically, when the input consists of ECG signals with a signal-to-noise ratio (SNR) of -4 dB, APtrans-CNN successfully increases the SNR to more than 6 dB, resulting in the diagnostic model's accuracy exceeding 96%.

Examining the Impact of Transformational Leadership in the Development of Organizational Voice An Analysis of the Mediating Impact of Information and Communication Technology

The existence of organizations that give importance to the role of the thoughts and ideas of individual members and allow the employees to express new opinions and ideas in accordance with the new developments of the information age; leaders are needed to be the pioneers of this important thing. The present study has been conducted with the objective of examining the influence of revolutionary leadership on the emergence of organizational voice with an investigation of the impact of information and communication technology. The research method is descriptive of the correlation type, to evaluate the data; the statistical methods of correlation, multiple regression and mediation of structural equations were used. The findings explained that transformational leadership has an important effect on both variables (organizational voice, information technology). Despite the truth that the coefficient of influence of two dimensions of motivation and social support is positive and more than other dimensions, also the level of influence of transformative leadership with the medium of negative information technology (-0.20) was obtained.

The First-person Effect of Green Advertising

Abstract Research on people’s perceptions of media effects had been focused on third-person effect of negative information, whereas the first-person effect of positive information was less explored. This paper aims to investigate the drivers of the first-person effect of green advertising and its behavioral consequences. An online questionnaire survey was used to collect data, obtaining 414 valid questionnaires that were analyzed using Partial Least Squares structural equation modeling. The result indicates that (1) the first-person effect existed in green advertising; (2) social desirability, environmental involvement, and advertising involvement had positive impact on the first-person effect, which in turn, led to green supportive behavior. In addition, this study proposes relevant theoretical implications and practical applications, as well as suggests for future research directions. JEL classification numbers: M37. Keywords: First-person effect, Social desirability, Environmental involvement, Advertising involvement.

Cognitive bias in voters' retrospective evaluations associated with COVID-19: evidence from Japan

Retrospective voting studies on catastrophic disasters can entail cognitive biases and politically affected approval/disapproval toward the government. In this study, I conducted an online survey experiment to assess the impact of providing individuals with information about COVID-19 infection status on their attitudes toward the government, governor, local administration, and national/local economic policies (n = 2,975). The findings indicated that the group exposed to positive information regarding infection status demonstrated a higher likelihood of adopting positive attitudes, while the group exposed to negative information showed a higher likelihood of adopting negative attitudes, despite receiving no other information. Although it was expected that individuals residing in prefectures with regular appearances of the governor on mass media platforms and that obtained COVID-19-related funding from the government would have a greater likelihood of experiencing such an attitude change, the findings do not corroborate these hypotheses.

Doomscrolling: A Review

COVID-19 pandemic has changed the daily life habits of individuals as well as affecting their physical health. Efforts to acquire information about the outbreak and combat uncertainty have led to an increase in individuals' consumption of negative news. Technology and social media platforms have facilitated access to this information, resulting in the emergence of a new term called "doomscrolling". Doomscrolling is defined as the act of spending excessive time online navigating through unpleasant news, despite it being saddening or demoralising. Among the underlying reasons for this behavior, the desire to cope with uncertainty and the quest for positive information stand out. Additionally, personality traits and mental health status emerge as significant factors influencing this behavior. Strategies to reduce or prevent doomscrolling involve controlling technology usage, finding positive content to balance negative information, and engaging in offline activities that positively impact mental well-being. Given ongoing wars, economic crises, natural disasters, and similar situations, individuals' desire for information may lead to doomscrolling behavior, posing a risk of negative effects. Therefore, efforts aimed at promoting conscious technology usage and content consumption habits could be a critical step in preventing doomscrolling.