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This paper delves into evaluating the effectiveness of warning messages as a deterrent against excessive fat consumption. It examines how consumers perceive the fat content of food products when presented with two distinct label types: (1) a textual warning, providing succinct information about the fat content, and (2) a pictorial warning, offering a visual representation that immediately signifies the fat content.

Two quantitative studies were carried out. Study 1 employed a questionnaire to evaluate the efficacy of textual and pictorial warning messages on high- and low-fat food products. Similarly, Study 2 replicated this comparison while incorporating a neuromarketing instrument to gauge participants’ cerebral reactions.

Results indicate that pictorial warnings on high-fat foods significantly deter consumers’ purchasing intentions. Notably, these pictorial warnings stimulate the left prefrontal area of the cerebral cortex, inducing negative emotions in consumers and driving them away from high-fat food items.

While the influence of images over text in shaping consumer decisions is well understood in marketing, this study accentuates the underlying mechanism of such an impact through the elicitation of negative emotions. By understanding this emotional pathway, the paper presents fresh academic and managerial perspectives, underscoring the potency of pictorial warnings in guiding consumers towards healthier food choices.

1. Textual warnings do not seem to discourage high-fat product consumption.

2. A pictorial warning represents the fat content of an equivalent product.

3. Pictorial warnings decrease the intention to purchase a high-fat product.

4. Pictorial warnings determine an increase in negative emotions.

Textual warnings do not seem to discourage high-fat product consumption.

A pictorial warning represents the fat content of an equivalent product.

Pictorial warnings decrease the intention to purchase a high-fat product.

Pictorial warnings determine an increase in negative emotions.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.

Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.

The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.

Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.

Non-verbal communication (NVC) remains largely understudied despite its importance in today's fast-paced and cross-cultural management and research landscape. This article is significant because it reveals valuable insights into NVC, which represents 65–93% (Mehrabian, 1981) of communication and has the potential to considerably increase management effectiveness and efficiency by providing leaders and researchers with the knowledge they need to understand and handle diversity with competence.

This article draws on social identity theory (SIT) (Tajfel and Turner, 1979) and rapport management theory (RMT) (Brown and Levinson, 1987) to analyse illustrative interview extracts of co-occurring verbal and NVC from an interpretative phenomenological analysis (IPA) study focussed on understanding how London-based Romanian migrant entrepreneurs experience acculturation.

Romanian migrant entrepreneurs use a variety of verbal and non-verbal communication approaches in their acculturation narratives, providing depth and occasionally shifting meaning. These tactics include repeating verbal discourse with non-verbal clues, replacing verbal communication with non-verbal gestures, complementing verbal communication and juxtaposing non-verbal cues with verbal descriptions.

This study makes a valuable contribution to the fields of qualitative organisational management and entrepreneurial studies by addressing the lack of methodological tools available for analysing non-verbal language in interpretative research. This study presents a systematic technique for assessing non-verbal language symbols that has been developed through face-to-face interviews. The article utilises the first-hand interview experience of a Romanian co-researcher to demonstrate the significance of NVC in the transmission of meaning and the formation of identities amongst Romanian migrant entrepreneurs. These findings contribute to a better understanding of organisational management and research practices, particularly about this understudied entrepreneurial minority of Romanian businesses in London, by helping researchers and managers better grasp the cultural and contextual meanings communicated non-verbally. The article holds significance in the context of cross-cultural and organisational management practices.