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Nuclear fusion energy is a solution to the substitution of fossil fuels and the global energy deficit. However, among the several problems encountered for realizing a nuclear fusion reactor, the divertor presents difficulties due to the tremendous heat flux (~10 MW/m2) from high-temperature plasma. Also, neutrons produce additional heat (~17.5 MW/m3) from collisions with the materials’ atoms. This may lead to unexpected effects such as thermal failure. Thus, a comprehensive investigation on the divertor module is needed to determine the heat-absorbing capacity of the divertor module so to maintain the effect of incident heat flux. In this study, using an analytical approach and a simulation, the quantitative effect of heat generation on the thermophysical behavior, such as temperature and thermal stress, was analyzed while maintaining the incident heat flux. Then, a correlated equation was derived from the thermal design criteria, namely, the maximum thimble temperature and the safety factor at the vulnerable point. Finally, on the basis of the thermal design criteria, the heat-absorbing capacity of a nuclear fusion reactor in operating conditions was determined. This study contributes to the understanding of the divertor’s effects in nuclear fusion reactors for high-heat-flux and high-temperature applications.

PurposeThis study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment.Design/methodology/approachWe proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used.FindingsThe effect of AI-generated misinformation on people’s perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users’ heuristics or is validated by the diagnosticity they perceive.Research limitations/implicationsWhen exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation.Practical implicationsPerceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people’s perceptions of misinformation and AI features is an efficient way to change their misinformation behavior.Social implicationsAlthough there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users’ cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread.Originality/valueThe mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167

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.

Prior research indicates that a brand’s product-harm crisis can lower consumer evaluations of competing brands within the same industry, which are known as negative spillover effects. Competing brands should proactively respond to the crisis to dissociate from the crisis-stricken brand. In the current research, two experiments were conducted to examine the relative efficacy of crisis response strategies (bolstering vs. differentiation vs. no response) in mitigating negative spillover effects on competing brands with different market positions (market leader vs. market challenger). The context of these experiments is a product-harm crisis in which a brand’s product is recalled due to food-borne illness. The moderated mediation analyses reveal that the bolstering strategy leads to similar positive indirect effects on brand attitude and purchase intention through message evaluations regardless of whether market leaders or challengers employ the strategy. In contrast, the differentiation strategy yields a more positive indirect effect on brand attitudes and purchase intentions through message evaluations when a market challenger employs the strategy rather than a leader.

PurposeThis study uses third-person effect theory to examine the mechanisms of public opinion about self-regulatory efforts to deal with COVID-19 vaccine-related misinformation on social media, focusing on the roles of social undesirability perceptions and misinformation beliefs.Design/methodology/approachA national survey of 600 US adults from the Qualtrics panel was conducted. The study examines how perceived social desirability and misinformation beliefs moderate the relationship between exposure to misinformation and behavioral responses.FindingsThe results show that the perceived disparity in misinformation exposure relates to third-person perception (TPP), which increases support for content moderation and intentions for corrective actions. Perceiving misinformation as socially undesirable strengthens the link between the exposure gap and TPP. Individual beliefs about misinformation are identified as a crucial moderator, reducing the TPP effect on those who have high misinformation beliefs, leading to less support for content moderation and corrective actions.Originality/valueThis research enhances understanding of TPP in the context of COVID-19 vaccine misinformation by highlighting how social undesirability perceptions and misinformation beliefs moderate this effect. It emphasizes the significance of personal misinformation beliefs in shaping attitudes toward content moderation and corrective actions.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2024-0220

A number of artificial intelligence (AI) systems have been proposed to assist users in identifying the issues of algorithmic fairness and transparency. These AI systems use diverse bias detection methods from various perspectives, including exploratory cues, interpretable tools, and revealing algorithms. This study explains the design of AI systems by probing how users make sense of fairness and transparency as they are hypothetical in nature, with no specific ways for evaluation. Focusing on individual perceptions of fairness and transparency, this study examines the roles of normative values in over-the-top (OTT) platforms by empirically testing their effects on sensemaking processes. A mixed-method design incorporating both qualitative and quantitative approaches was used to discover user heuristics and to test the effects of such normative values on user acceptance. Collectively, a composite concept of transparent fairness emerged around user sensemaking processes and its formative roles regarding their underlying relations to perceived quality and credibility. From a sensemaking perspective, this study discusses the implications of transparent fairness in algorithmic media platforms by clarifying how and what should be done to make algorithmic media more trustable and reliable platforms. Based on the findings, a theoretical model is developed to define transparent fairness as an essential algorithmic attribute in the context of OTT platforms.

The present study was undertaken to determine the prevalence of Salmonella in 100 chicken carcass samples from five integrated broiler operation brands in Korea. Serotypes, antibiotic resistance patterns, extended-spectrum β-lactamase (ESBL) genotype, and clonal divergence using multilocus sequence typing of the isolated strains were analyzed. A total of 42 chicken samples were contaminated with nontyphoidal Salmonella (NTS) isolates: 16 isolates (38%) were Salmonella Virchow, 9 (21%) were Salmonella Bareilly, and 8 (19%) were Salmonella Infantis. A multidrug resistance (MDR; resistant to more than three classes of antibiotics) phenotype was observed in 29% of the isolates, which were resistant to five or more classes of antibiotics. The dominant MDR type was resistance to classes of penicillin, cephalosporins, aminoglycosides, quinolones, and tetracyclines. All the MDR isolates were positive for ESBL producers, and all but one (with the CTX-M-1 genotype) had the CTX-M-15 genotype. Multilocus sequence typing of the isolates revealed ST16 as the dominant sequence type; Salmonella Virchow, Salmonella Infantis, and Salmonella Richmond were all ST16, indicating a close genetic relationship between these serovars. This is the first study in Korea showing the CTX-M-1 type of NTS and the prevalence of ESBL-producing strains among NTS isolated from retail chicken meat. Our findings suggest that MDR Salmonella contamination is widely prevalent in retail chicken meat, and consumption of inadequately cooked products could lead to dissemination of NTS, which is hazardous to human health.

Grounded in Channel Complementarity Theory (CCT), this study assumes that users perceive Generative AI (GenAI) tools not as replacements but as complementary sources for seeking health information. Applying the push-pull-mooring (PPM) framework from information systems research, it examines how perceived information overload with traditional search engines (push factor), the perceived credibility of GenAI-generated health information (pull factor), and perceived hallucination risk (mooring factor) are associated with current users’ intentions to continue using GenAI tools, with comparative usefulness serving as a mediating factor. Using survey data from a nationally representative sample of 1,200 adults in South Korea, the proposed model was tested using PLS-SEM. The results supported all hypotheses: perceived overload and credibility were positively associated with perceived comparative usefulness, while hallucination risk was negatively associated with it. Both overload and credibility were directly and indirectly related to continuance intention, whereas hallucination risk was associated with continuance intention only indirectly through perceived usefulness.

Having observed the proliferation of corporate stealth campaigns employing parody videos on YouTube, we attempted to examine the effectiveness of refutational preemption in providing viewers with ability to detect unduly manipulative intent and in conferring viewer resistance to the influence of the campaign. We also compared the relative efficacy of the inoculation pretreatment to post-hoc refutation. Results showed that subjects who received a preemptive inoculation message were better able to detect unduly manipulative intent in a parody video, were more resistant to altering their original attitudes toward the issue, and demonstrated less favorable attitudes toward the sponsor of the parody video than did their counterparts in the control group.

Multinational corporations (MNCs) are increasingly dominating the world economy. U.S. companies have expanded their markets by opening foreign subsidiaries in ever-increasing numbers. European and East Asian companies have also extended their reach since the late 1980s by opening subsidiaries across the globe (Shuter & Wiseman, 1994). According to Bartlett and Ghoshal (2000), MNCs account for more than 40% of the world’s manufacturing output and almost a quarter of world trade. About 85% of the world’s automobiles, 70% of computers, 35% of toothpaste, and 65% of soft drinks are produced and marketed by MNCs.