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In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current study explores how consumers process information about fashion products displayed on different sizes of models in advertisements, focusing on model and consumer body sizes and both genders. As an underlying mechanism explaining how the relationship between model and consumer body sizes shapes consumer purchase intention, this study explores the role of guilt, shame and mental imagery.

The current study uses a text analytics technique to identify female consumers' general opinions of thin models in advertising. Employing a 3 (consumer body size: normal, overweight, obese) × 2 (model body size: thin, plus-size) × 2 (gender: male, female) between-subjects online experiment (n = 718), the main study comparatively analyzes the influences of plus-size and thin models on consumer responses.

The results reveal that, despite body positivity movements, thin models still generate negative emotions among female consumers. For obese female consumers, advertisements featuring plus-size models produce fewer negative emotions but not more mental imagery than advertisements featuring thin models. Conversely, for obese male consumers, advertisements featuring plus-size models generate more mental imagery but not more negative emotions than advertisements featuring thin models. The results also reveal that the relationship between consumer body size and guilt is moderated by perceived model size, which is also moderated by gender in generating mental imagery. While guilt plays a mediating role in enhancing mental imagery, resulting in purchase intention, shame does not take on this role.

This study is the first to present an integrated model that elucidates how consumers with varying body sizes respond to different sizes of models in advertising and how these responses impact purchase intentions.

Our findings only apply to contexts where consumers purchase fashion clothing in response to advertisements featuring thin versus plus-size models.

Exposing normal-size consumers to plus-size models generates less mental imagery, and thus, practitioners should seek to match the body sizes of the models featured in advertising to the body sizes of their target audience or ad campaigns that include both plus-size and thin models may help improve message persuasiveness in fashion advertising. Moreover, guilt-appeal advertising campaigns using thin models would appeal more to thin consumers of both genders than shame-appeal advertising.

Tourism via virtual reality (VR) technology has become an interesting option for consumers to “travel.” The best approaches to optimizing the VR tourism environment, improving the interactive experience of tourists and encouraging tourists to adopt VR are not yet fully understood. This study explores the willingness of tourists to adopt VR tourism from the dual aspects, richness and dynamics, of virtual social cues.

To examine the effects of richness (multiple vs. few cues) and dynamic (changeable vs. static cues) on consumers' willingness to adopt VR tourism, three virtual tourism scenes were designed and presented by head-mounted displays. The data were collected for participants in the VR laboratory and tested by ANOVA and partial least squares–structural equation modeling.

Virtual social cues can generate mental imagery through interactivity, vividness and parasocial interactions, thus increasing the consumer's likelihood of adopting VR tourism. It was also found that imagination moderates mental imagery and adoption intention. When the consumer's imagination is stronger, their mental imagery stimulates a stronger willingness to adopt VR tourism.

The authors innovatively utilize concepts of parasocial interaction and mental imagery and discuss the various influences and mediation mechanisms of social cue characteristics on consumers' adoption of VR tourism. The conclusions may provide new insights for VR tourism managers and tourism scholars.

The purpose of this study is to investigate the effects of visual and verbal information presentations on mental imagery, perceived informativeness and purchase intention. The study assesses two types of product-related information: (1) visual information: static product images and augmented reality (AR) and (2) verbal information: abstract and concrete product reviews.

A total of 320 mobile consumers participated in the experiment. To increase external validity, this study was conducted in an existing digital shopping environment.

The results suggest that AR has a greater effect on consumers' shopping outcomes than static images. The findings further reveal that concrete product reviews are important in increasing mental imagery, perceived informativeness and purchase intention when visual information does not provide an AR function.

This study contributes to the current literature by providing empirical support for AR effects and concrete reviews on consumer responses. The results further provide an important perspective for retailers seeking ways to develop effective information presentations in digital retailing.

Despite entrepreneurial linguistic style gaining increased attention in entrepreneurship studies, the field for digital vlogger entrepreneurs still lacks a comprehensive understanding of how linguistic patterns enhance audiences attitude and behaviour. This study aims to propose a conceptual model of “language-mental imagery-attitude-behaviour model” that leads to the examination of rich sensory language style of food travel vlogger entrepreneurs and its persuasive effect on audiences' attitude and behavioural intention.

The present study utilises a stimulus-based survey method that involves a sensory-rich vlog script extracted from a high social media engagement authentic vlog. Data are collected through an online questionnaire distributed to a sample of 355 participants via the Amazon Turk mechanism. The study employs confirmatory factor analysis and structural equation modelling to test the proposed hypotheses, with the aim of contributing to the advancement of theories of embodied cognition in entrepreneurial language by examining the attitudes and behaviours of audiences exposed to sensory-rich language. The findings of this research provide valuable insights into the effects of sensory-rich language on audience responses and can inform future research on the role of embodied cognition in entrepreneurial communication.

The findings demonstrate that vlogger entrepreneurial sensory-rich linguistic communication style positively influence audiences' attitude, behavioural involvement with food and intention to taste. Visit intention will be enhanced via the mediating effects of attitude, behavioural involvement with food and intention to taste.

This research highlights the significance of sensory-rich language for vlogger entrepreneurs in entrepreneurial communication, digital storytelling and for destination marketing enterprises in creating a digital sensory engagement marketing strategy.

The study contributes to the literature by elucidating the theories of embodied cognition in entrepreneurial communication. By examining the relationships between vlogger communication evoked mental imagery, audiences attitude and behaviours, this study provides novel insights into the effectiveness of sensory-rich language in vlogger entrepreneurial communication and its impact on audience engagement. These findings have important implications for communication scholars and practitioners alike, shedding light on the role of embodied cognition in entrepreneurial language and the potential of sensory-rich language to enhance audience engagement.

Despite the growing importance of visual information, user-generated photos in product reviews have received relatively little attention. We investigate whether the contextual background of a product image can influence consumers' perceptions of review helpfulness and product evaluation.

Online experiments were conducted using a scenario technique. A single factor (contextual background: low vs. high) between-subjects design was conducted in Study 1. A 2 (contextual background: low vs. high) × 2 (mental simulation: outcome vs. process) between-subjects design was conducted in Study 2.

A photo with a high (vs. low) contextual background enhances mental imagery, increasing perceived helpfulness and product evaluation. Furthermore, mental simulation plays a significant moderating role in the relationship between contextual background and mental imagery.

Based on cue utilization theory, this study identifies how the contextual background of product images affects consumers' perception and product evaluation by uncovering the underlying mechanism of mental imagery. Furthermore, the research examines the moderating effect of mental simulation while reviewing user-generated photos.

This paper aims to investigate the differential effects of vertical attributes and horizontal attributes on visit intention under proximal and distal sensory imagery in travel live streams.

This research used a multimethod approach with four studies. Three designed experiments were first employed to prove casual relations of the hypothesized relations. Then, a structural model provided a new sample of the framework.

The results suggest that visit intention is higher when vertical (vs horizontal) attributes are associated with proximal (vs distal) sensory imagery. Physical presence mediates the interaction effects between attribute type and sensory imagery on visit intention.

The finding offers suggestions for multilateral information providers' capability of real-time advertising, seller-focused technology development and proactive relationship management with potential consumers.

Previous study is less sufficient to describe consumers' traveling interactivities in live-streaming media, where streamers are capable of modifying attribute-based messages and sensory modalities. Rather than focusing on imagery as a comprehensive modality or visual-dominated imagery, this study examines the interaction effects between attribute type and sensory imagery on visit intention. Drawing on reason-based choice and distance-on-distance theories, the finding enriches the evaluation of the effectiveness of live-streaming marketing across varying sensory interactions.

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.

According to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.

To validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.

The proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

This study aims to examine the effects of three self‐congruity constructs: the brand's personality congruity (BPC), the brand's user imagery congruity and the brand's usage imagery congruity, in consumers' attitude and brand loyalty toward two luxury fashion brands.

Using a sample of Australian consumers, this study examines two luxury fashion brands (CK and Chanel) from two product categories, watches and sunglasses. Structural equation modeling is used to test the hypotheses.

This study finds that user and usage imagery congruity are stronger predictors for brand attitude and brand loyalty than BPC in the context of the luxury fashion brands tested. Both user and usage imagery congruity have significant effects in brand attitude and brand loyalty in most analyses. This study finds no significant effect of BPC in either brand attitude or brand loyalty for the two brands tested.

Future studies should include more populations, product categories and more brands in each category.

Symbolic benefits are key motivations behind luxury brand purchases. Symbolic benefits are from non‐product‐related attributes like imagery. One important implication of the study is that user and usage imagery are more important to build than attempts to develop a brand's personality. Because most luxury brands market in multiple product categories, attention should be paid to the core perceptions of user and usage imagery for the brand when designing communication strategies for different categories.

This study provides the first evidence that these self‐congruity concepts may represent different imageries that lead to different effects in brand attitude and brand loyalty. Findings from this study add to the understanding of the consumption of luxury brands.