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Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

This study aims to explain the model of impulsive purchase behavior in the Islamic Republic of Iran’s tourism industry.

This study uses the inductive–deductive approach for data collection. Field (interviews) and library (articles, books and theses) methods have been used for data collection. After determining the research variables, content analysis units (theme, category and markers) are specified. Academic experts and professionals in the subject area helped during the model design stage.

The results reveal 14 visual attention variables which are classified into four categories: attractiveness in the market, emotional reactions, improving the shopping style of tourists and visual advertising.

This study focuses only on Iran. There is scope to expand the discussion with more interviews. The impact of the COVID-19 pandemic on tourism presented some unique challenges in participant recruitment. Future studies could focus on other Middle Eastern countries or other international areas. Moreover, future researchers could analyze other variables affecting impulsive purchase.

No other studies on tourism industry marketing have recognized the effective variables in impulsive purchase. To fill this gap, this paper explains the visual attention model in tourists’ impulsive purchase behavior.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.

Metaverse, that is extended reality (XR)-based technologies such as augmented reality (AR) and virtual reality (VR), are increasingly believed to facilitate fundamental human practice in the future. One of the vanguards of this development has been the consumption domain, where the multi-modal and multi-sensory technology-mediated immersion is expected to enrich consumers' experience. However, it remains unclear whether these expectations have been warranted in reality and whether, rather than enhancing the experience, metaverse technologies inhibit the functioning and experience, such as cognitive functioning and experience.

This study utilizes a 2 (VR: yes vs no) × 2 (AR: yes vs no) between-subjects laboratory experiment. A total of 159 student participants are randomly assigned to one condition — a brick-and-mortar store, a VR store, an AR store and an augmented virtuality (AV) store — to complete a typical shopping task. Four spatial attention indicators — visit shift, duration shift, visit variation and duration variation — are compared based on attention allocation data converted from head movements extracted from recorded videos during the experiments.

This study identifies three essential effects of XR technologies on consumers' spatial attention allocation: the inattention effect, acceleration effect and imbalance effect. Specifically, the inattention effect (the attentional visit shift from showcased products to the environmental periphery) appears when VR or AR technology is applied to virtualize the store and disappears when AR and VR are used together. The acceleration effect (the attentional duration shift from showcased products to the environmental periphery) exists in the VR store. Additionally, AR causes an imbalance effect (the attentional duration variation increases horizontally among the showcased products).

This study provides valuable empirical evidence of how VR and AR influence consumers' spatial bias in attention allocation, filling the research gap on cognitive function in the metaverse. This study also provides practical guidelines for retailers and XR designers and developers.

Artifacts are rarely used today to visualize thoughts, insights, and ideas in strategy work. Rather, textual and verbal communication dominates. This is despite artifacts and visual representations holding many advantages as tools to create and make sense of strategy in teamwork. To advance our understanding of the benefits of visual aids in strategy work, I synthesize insights from cognitive psychology, neuroscience, and management research. My analysis exposes distinct neurocognitive advantages concerning attention, emotion, learning, memory, intuition, and creativity from visual sense-building. These advantages increase when sense-building activities are playful and storytelling is used.

Understanding the effect of assortment composition on attentional levels is an essential topic for academic researchers and practitioners. This work has important implications when analyzing the influence of shopping frame time and search effort on the relationship between the reaction to assortment composition and visual attention to stock-keeping units (SKUs) pricing.

Two experimental studies through gauze behavior analysis technology (using eye-tracking equipment) analyze the variable's large assortment, visual attention to SKU pricing, search effort and shopping frame time.

The results suggest that, although it increases the search effort, a large assortment decreases the visual attention to SKU pricing. Further, our results indicate a moderating effect associated with mitigating the negative effect by medium-low levels of search effort and a moderating impact of time in this relation.

Marketing professionals can carefully optimize the in-store experience by managing the assortment and variety and by influencing consumers' visual attention to SKU pricing along the journey as part of the experience. Assortment and SKU pricing strategies need to be aligned with consumer journey design.

Our findings contribute to assortment theory and management by detailing the relationship between consumers' reactions to assortment perception and visual attention to SKU pricing in time flow. We reinforce the importance of considering assortment strategies from the consumer perspective and giving reliable information about in-store behavior.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The author examined effects of endorser type and message framing on visual attention and ad effectiveness in health ads, including the moderator of involvement. This paper aims to discuss this issue.

An experiment was conducted with a 2 (celebrity vs. expert) × 2 (positive vs. negative framing) between-subject factorial design. Eye-tracking measured visual attention and a questionnaire measured ad effectiveness and product involvement.

Experimental data from 78 responses showed no vampire effect in the health advertisements. Celebrity endorsement with negative message framing received more attention and had less ad recall than that with positive message framing. Negative and positive message framing attracted the same amount of attention and ad recall in the expert endorsement condition. High involvement participants paid more attention to the ad message with the expert than that with the celebrity, but ad recall was not significantly increased. Low involvement participants exhibited the same attention to the ad message with the expert and with the celebrity, but had greater recall of the ad message with the expert. Visual attention to the endorser was associated with ad attitude but not with ad recall. Ad attitude impacted behavioral intention.

Studies examining influences of celebrity and message framing on ad effectiveness have focused on the response to advertising stimuli, not the information process. The author provides empirical evidence of the viewers' information processing of endorsers and health messages, and its relationship with ad effectiveness. The study contributes to the literature by combining endorser and message framing in health ads to promote public health communication from the information processing perspective.

Based on sensory marketing theory and cognitive appraisal theory, this study investigates whether and how the background visual complexity of live-streaming affects consumers' purchase intention and reveals the underlying mechanisms through which background visual complexity influences consumers' purchase decisions.

The experiment was conducted with 180 college students, using eye-tracking technology to explore the impact mechanism of live background visual complexity on consumers' purchase intention, considering three types of background visual complexity (high vs medium vs low) and two levels of need for cognitive closure (high vs low).

Firstly, the background visual complexity of live-streaming positively influences consumers' purchase intention by eliciting positive emotions (pleasure and arousal), and the relationship between consumer emotions and purchase intention is nonlinear. Secondly, need for cognitive closure to significantly moderate the influence of background visual complexity on purchase intention.

The limited sample size makes it difficult to generalize to other consumer groups. Also, the study only focuses on one visual factor, lacking comprehensive analysis from multiple perspectives.

It is recommended that live e-commerce companies optimize the visual design of live-streaming backgrounds and identify consumer traits to match the visual complexity with consumers' level of need for cognitive closure, thereby stimulating positive emotions and facilitating more satisfactory shopping decisions.

This paper addresses an interesting and practical issue related to the effects of live background visual complexity on consumers' purchase intention.

Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.