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PurposeDrawing on the third-person effect (TPE) theory and the theory of planned behavior (TPB) as a theoretical framework, the current study aims to explore the cognitive mechanisms behind how third-person perception (TPP) of misinformation about public health emergencies affects intention to engage in corrective actions via attitude, subjective norms and perceived behavioral control.Design/methodology/approachA total of 1,063 participants in China were recruited via a professional survey company (Sojump) to complete an online national survey during the outbreak of coronavirus (COVID-19) in China. Structural equation modeling using Mplus 7.0 was used to address the research hypotheses.FindingsThe results reveal that attention to online information about public health emergencies significantly predicted TPP. In addition, TPP positively influenced attitude and perceived behavioral control, which, in turn, positively encouraged individuals to take corrective actions to debunk online misinformation. However, TPP did not significantly influence subjective norms. A potential explanation is provided in the discussion section.Research limitations/implicationsThe research extends the TPE theory by providing empirical evidence for corrective actions and uncovers the underlying cognitive mechanism behind the TPE by exploring key variables of the TPB as mediating constructs. These are all significant theoretical contributions to the TPE and offer practical contributions to combating online misinformation.Originality/valueThe research extends the TPE theory by providing empirical evidence for a novel behavioral outcome (i.e. corrective actions in response to misinformation) and uncovers the cognitive mechanism underlying the TPE by exploring key variables of the TPB as mediating constructs. These are all significant theoretical contributions to the TPE and offer practical contributions to combating online misinformation.

Research summary: We conducted a real-time field study of a post-acquisition integration process. We identified two practices that contributed to integration failure. First, the practice of masking negative emotions caused members of both firms to perceive that the partner firm's members were satisfied with the integration process, even though they were not. These false perceptions of satisfaction resulted in minimal corrective actions, the escalation of the situation, and ultimately, integration failure. Second, efficiency-driven communication practices used in interfirm communication exacerbated the effect of masking negative emotions on false perceptions of satisfaction by shielding both firms' members from the other firm's members' spontaneous emotional reactions. Our research invites scholars to consider more deeply the emotional consequences of various common organizational practices. Managerial summary: Do you disagree with your employee, manager, or business partner? Does this disagreement make you annoyed or even angry? Yet, you decide to mask your negative emotions and discuss the disagreement with a neutral or happy face. Wrong. We find that masking negative emotions in the work environment can prevent corrective actions, escalate disagreement, and make people to develop long-lasting negative sentiments toward the counterpart that ultimately result in dysfunctional behaviors. Our findings further reveal that commonly used organizational practices such as communicating via email may contribute to the deliberate masking of negative emotions. We suggest that managers should carefully review if and how their organization's practices prevent or enable people to share their emotions authentically to ensure timely corrective actions and proactive development of business operations.

Clinical trials are designed to evaluate the efficacy, safety, or other characteristics associated with medical products. Trials are usually complex and require a large group of professionals to follow a clinical trial protocol, standard operating procedures, and study-specific manuals, guidelines, and plans. Clinical trial protocols prospectively describe the background and rationale for conducting the trial, the objectives of the trial, the trial design, the equipment to be used, the procedures to be performed, and the statistical methods on how the trial data are to be analyzed. Deviations from the protocol can result in harm to subjects, biased or inaccurate results, and possible rejection of all or part of the trial data by the sponsor or regulatory authorities. Despite preventive efforts, protocol deviations are likely to occur in most trials. This position paper proposes a common definition of protocol deviations and recommends best practices for their detection, classification, and management as part of their life cycle, with a goal of reducing their impact on subject safety and data integrity. The information contained herein is drawn globally from industry experts within the DIA Good Clinical Practice and Quality Assurance community, an industry-wide survey, and presentations with discussions at various industry meetings.

A conventional LASSO (least absolute shrinkage and selection operator) regression model utilizing the Pythagorean fuzzy sets in a system reliability analysis is developed. Overall, the Pythagorean fuzzy multivariate regression analysis enables decision makers to correctly identify the relationships between a set of responses in the form of fuzzy or non-fuzzy interpretive variables. The interpretability of the model is significantly improved by the proposed Pythagorean fuzzy LASSO regression model (PFLRM). Thus, a system reliability analysis is considered as an application of the study to evaluate the efficiency and effectiveness of the proposed PFLRM. There is no doubt that a system reliability analysis is vital to improve the safety performance of chemical processing industries, where an extensive number of industrial accidents occur annually. These accidents have subsequently highlighted the failure of some of the intervention actions to keep the systems safely in operation. The results illustrate a better performance with higher accuracy with the proposed PFLRM compared with the existing number of fuzzy regression models, particularly in the availability of non-informative variables.

Misinformation is a pervasive problem in the social media era, undermining social trust, while the emergence of AI-generated misinformation further exacerbates this issue and threatens social order. This study employs the third-person effect as a theoretical framework and collected 726 questionnaire responses to examine individuals’ perceptions of AI-generated misinformation. When exploring the relationships among misinformation exposure, social undesirability, perceived realism, and AI literacy with respect to perceived effects on oneself and on others, individuals recognize that they themselves are also easily influenced by AI-generated misinformation. Furthermore, perceived effects on oneself and others are positively correlated with corrective actions, while no significant relationship is observed with restrictive actions, thus providing suggestions for individual involvement in combating AI-generated misinformation.

Based on the cognitive mediation model (CMM), this study seeks to examine how attention to different media platforms influenced different knowledges via reflective integration, ultimately motivating individuals to perform corrective and restrictive actions against misinformation in the context of healthy eating misinformation. Using data collected from a national survey of 563 Chinese citizens, the findings of this study are threefold. First, attention to television and social media stimulated elaboration and interpersonal communication, while attention to websites only elicited elaboration. Second, only structural and subjective knowledge, not factual knowledge, were found to motivate individuals to perform corrective and restrictive actions. Third, a multigroup analysis demonstrated that the effects of (a) attention to TV news on elaboration, (b) attention to websites on elaboration, (c) interpersonal communication on factual knowledge, and (d) structural knowledge on restrictive actions differed among participants with different levels of altruism. Theoretically, whereas previous studies have focused on single dimension of knowledge, this study uncovered the multi-dimensional nature of knowledge by exploring factual knowledge, structural knowledge, and subjective knowledge in the CMM framework. Moreover, based on the O-S-R-O-R model, the CMM could be extended to behavioral outcomes, which have been overlooked by most CMM studies. In response, this study extends the CMM by integrating corrective and restrictive actions as behavioral outcomes. Lastly, rather than assuming individuals as homogenous in previous research, this study delves into exploring how individuals at the average age of 33.37 ( SD  = 8.46) with different levels of altruism engaged in different processes of cognitive mediation.

Persistent Listeria monocytogenes contamination may occur in a packinghouse if the pathogen successfully infiltrates the facility and reaches a harborage site, where it may be difficult to remove and may contaminate produce within the facility. There is a need for simulation-based decision support tools that can predict which equipment sites are more likely to undergo persistent contamination and simulate potential corrective actions to prevent this contamination. Thus, we adapted for longer term simulation two existing applications of an agent-based model of Listeria spp. hourly contamination dynamics in produce packinghouses. Next, we developed a novel approach to identify and analyze persistent and transient Listeria contamination patterns on simulated agents representing equipment sites and employees. Testing of corrective actions showed that methods that involved targeted, facility-specific, risk-based sanitation were the most effective in reducing both the likelihood and duration of persistent contamination. Generic approaches to controlling Listeria (e.g., more concentrated sanitizers) are unlikely to be successful and suggest that use of sanitation schedules produced through facility-specific root cause analysis and hygienic design are key in reducing persistence. Hourly Listeria contamination patterns also suggest that transient contamination may be mistaken for persistent contamination, depending on the frequency of environmental sampling. Likewise, as concentrations of Listeria on most contaminated agents were predicted to be very low, there is also a possibility to mistake persistence for transient contamination of sites, or even miss it outright, due to false-negative environmental Listeria monitoring results. These findings support that agent-based models may be valuable decision support tools, aiding in the identification of contamination patterns within packinghouses and assessing the viability of specific corrective actions. Contamination source affects the frequency and duration of persistent contamination.Modifying agent cleanability is most effective in reducing frequency of persistence.Reduced contamination of incoming raw produce had no effect on persistence.Root cause analyses to identify plant-specific harborage sites are essential.

Using survey data with a national representative of U.S. adult women, the current study tested both the perceptual and behavioral hypotheses for the third-person effect of online advertising of cosmetic surgery (OACS) in a theoretical process model. A strong third-person perception (TPP) was observed in assessing the influence of OACS. The results of a path analysis revealed that the self–other exposure gap and social undesirability were positive predictors of the TPP. TPP had a direct impact on support for regulation (SFR) of OACS and an indirect effect on corrective actions. Both SFR and online political self-efficacy (OPSE) were the good predictors for corrective actions.

The production of plastics and associated products, including microplastics (MPs), has been surging over the past several decades and now poses a grave environmental threat. This is because when not appropriately recycled, incinerated, or disposed of in fully contained landfills, plastic waste manifests as a potent pollutant, with vast amounts finding their way into oceans annually, adversely impacting marine life and ecosystems. Additionally, research also confirms there are direct impacts from MPs on water, air, and soil, impacting ecosystem and human health. This study investigated all aspects of plastics and microplastics such as their generation and consumption, their presence in oceans, and their ultimate fate. Next, a comprehensive literature search was performed to identify impacts MPs have on watercourses and soils and eventually on the ocean, taking into consideration the coupled impacts of metals and emerging contaminants adsorbed onto MPs. Then, a model to estimate the number of MPs in oceans and then using toxicity of MPs to humans and aquatic life to estimate when oceans would become toxic to humanity is described. Utilizing the model, it is possible to estimate the year when MPs in the ocean could potentially become broadly toxic, for both humanity and marine life, under different emissions scenarios. The estimates conclude that with the current MP discharge growth, oceans would become toxic to humanity between 2398 and 2456, for MP discharge growth only until 2020, it could be reached between 2408 and 2472, and for emissions ending in 2020, oceans would not become toxic to the humanity. Finally, remediation strategies are described to prevent oceans from becoming toxic to humanity by focusing on various action items such as education and awareness, reducing the utilization of single-use plastic, and conventional and innovative strategies that can be used for the treatment of stormwater and wastewater.

For several years, the increase of extreme meteorological events due to climate change, especially in unusual areas, has focused authorities and stakeholders attention on electric power systems’ resilience. In this context, the authors have developed a simulation model for managing the resilience of electricity distribution grids with respect to the main threats to which these infrastructures may be exposed (i.e., ice sleeves, heat waves, water bombs, floods, tree falls). The simulator identifies the more vulnerable network assets by means of probabilistic indexes, thus suggesting the best corrective actions to be implemented for resilience improvement. The fulfillment of grid constraints, i.e., loading limits for branches and voltage limits for buses, under actual operating conditions, is taken into account. Load scenarios extracted from available measurements are evaluated by means of load flow analyses in order to choose, among the best solutions identified, those compatible with the constraints. The proposed tool can assist Distribution System Operators (DSOs) in drawing up the Action Plan to improve, on one hand, the resilience of the network and, on the other hand, to remove any possible limitation for the adoption of the best solutions to ensure maximum operational continuity during extreme weather events.