Search results

Dynamic advertising, including television and online video ads, demands new theory and tools developed to understand attention to moving stimuli. The purpose of this study is to empirically test the predictions of a new dynamic attention theory, Dynamic Human-Centred Communication Systems Theory, versus the predictions of salience theory.

An eye-tracking study used a sample of consumers to measure visual attention to potential areas of interest (AOIs) in a random selection of unfamiliar video ads. An eye-tracking software feature called intelligent bounding boxes (IBBs) was used to track attention to moving AOIs. AOIs were coded for the presence of static salience variables (size, brightness, colour and clutter) and dynamic attention theory dimensions (imminence, motivational relevance, task relevance and stability).

Static salience variables contributed 90% of explained variance in fixation and 57% in fixation duration. However, the data further supported the three-way interaction uniquely predicted by dynamic attention theory: between imminence (central vs peripheral), relevance (motivational or task relevant vs not) and stability (fleeting vs stable). The findings of this study indicate that viewers treat dynamic stimuli like real life, paying less attention to central, relevant and stable AOIs, which are available across time and space in the environment and so do not need to be memorised.

Despite the limitations of small samples of consumers and video ads, the results of this study demonstrate the potential of two relatively recent innovations, which have received limited emphasis in the marketing literature: dynamic attention theory and IBBs.

This study documents what does and does not attract attention to video advertising. What gets attention according to salience theory (e.g. central location) may not always get attention in dynamic advertising because of the effects of relevance and stability. To better understand how to execute video advertising to direct and retain attention to important AOIs, advertisers and advertising researchers are encouraged to use IBBs.

This study makes two original contributions: to marketing theory, by showing how dynamic attention theory can predict attention to video advertising better than salience theory, and to marketing research, showing the utility of tracking visual attention to moving objects in video advertising with IBBs, which appear underutilised in advertising research.

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.

The purpose of this paper is to explore the associations between food literacy, consumer profiling and purchasing behaviour in a sample of Italian consumers.

Participants (N = 194) completed an online survey including personal data, two questionnaires on purchase behaviour and food consumption, the General Trust Scale (GTS), a questionnaire assessing individual chronotype and two scales about food literacy: one investigating nutritional knowledge (short food literacy questionnaire, SFLQ) and the other focussing on procedural skills (self-perceived food literacy scale, SPFL). Associations between food literacy, consumer profiling and purchase behaviour were analysed with linear regression models.

Participants with specific education in nutrition reported higher scores in food literacy. The final score of food literacy was predicted by a greater attention to nutritional content and nutritional properties of products. Women paid more attention to nutritional properties than men, and they obtained higher scores in SFLQ. Evening types obtained lower scores in SFPL compared to intermediate and morning chronotypes. Body mass index (BMI) was negatively correlated to SPFL score, while it was associated with the easy availability of a product, so that obese (BMI ≥ 30) subjects considered the easy availability of a product more important compared to non-obese ones (BMI < 30).

This study investigates the influence of personal and psychometric variables of consumer profiling on food literacy and consequently on purchase behaviour, paving the way for implementing healthier food consumption policies. These findings reinforce the primacy of specific education in building healthy eating habits.

This study explores the effect of video assistant referee (VAR) sponsorship on spectator response and compares it with advertising and conventional sponsorship.

An experiment with 809 subjects is conducted by analyzing 20 one-minute video clip stimuli from a Premier League soccer game divided into four formats: two formats of VAR sponsorship, advertising, and conventional sponsorship.

The results show that the indicators of recall, credibility, and perceived congruence improve when the VAR sponsorship format is used.

This is the first manuscript to examine the effectiveness of a new type of sponsorship: VAR sponsorship. This manuscript provides metrics that will guide practitioners on whether to use this type of sponsorship.

This study aims to consider active vision in low-visibility environments to reveal the factors of optical properties which affect visibility and to explore a method of obtaining different depths of fields by multimode imaging.Bad weather affects the driver’s visual range tremendously and thus has a serious impact on transport safety.

A new mechanism and a core algorithm for obtaining an excellent large field-depth image which can be used to aid safe driving is designed and implemented. In this mechanism, atmospheric extinction principle and field expansion system are researched as the basis, followed by image registration and fusion algorithm for the Infrared Extended Depth of Field (IR-EDOF) sensor.

The experimental results show that the idea we propose can work well to expand the field depth in a low-visibility road environment as a new aided safety-driving sensor.

The paper presents a new kind of active optical extension, as well as enhanced driving aids, which is an effective solution to the problem of weakening of visual ability. It is a practical engineering sensor scheme for safety driving in low-visibility road environments.

The purpose of this paper is to perform fine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions.

A driving simulator experiment was conducted to collect data of speed and lane position. ANOVA was used to explore the difference in driving behavior under different visibility conditions.

The results show that only average speed is significantly different under different visibility conditions. With the visibility reducing, the average vehicle speed decreases. The road visibility conditions in a straight segment can be divided into five levels: less than 20, 20-30, 35-60, 60-140 and more than 140 m. The road visibility conditions in a curve segment can be also divided into four levels: less than 20, 20-30, 35-60 and more than 60 m.

A fine classification of road traffic visibility has been performed, and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.

The main purpose of this study is to analyze how consumers’ visual attention to wine label design correlates with their preferences. Accordingly, this study uses quantitative eye-tracking metrics to understand which design proposal has greater visual salience. A more specific objective was to assess which design proposal was preferred to be marketed.

The experiment involved evaluating of three different labeling proposals of an Italian winery. Infrared eye-tracking was used to measure implicit eye movements on the three bottles displayed, simultaneously, on a computer screen. A generalized linear model was used to test how consumers' visual attention to wine label design correlated with their preferences.

The design proposals were evaluated significantly differently, with one set being preferred. In general, a strong positive relationship was found between pausing to peruse a specific design proposal and making an explicit choice of the same bottle.

The main limitation of the experiment concerns the sample interviewed. As the sample is homogeneous, the results may not be generalizable to other segments. Furthermore, the addition of electroencephalographic devices that monitor brain activity could provide crucial information for understanding consumer behavior during the purchase decision-making process.

Eye-tracking methods could be useful for designers and wine producers during the evaluation process of design projects.

The use of eye-tracking for evaluating design proposals before placing a product on the market is relatively novel. This method provides objective, quantitative and predictive information on consumer preferences contributing guidelines to designers and marketers during the product conception phase.

This study aims to analyse the effectiveness of a static promotional banner located on a hotel reservation website in terms of capturing the visitor’s visual attention by exploring how this impact depends on the user’s degree of internet experience.

An experiment was conducted using the eye-tracking methodology, in addition to a self-administered questionnaire. Through eye-tracking technology, eye movements were recorded whilst participants explored a generic hotel website. The factors used in the analyses were the position of the banner on the website and participants’ experience as internet users.

The findings showed that positioning a banner at certain locations on the webpage may lead to a better recall, which, in part, seems to result from the visual attention that such locations attract. The mediation analysis showed that the bottom-right and bottom-left positions have a negative effect on banner recall due, in part, to the shorter attention times and the smaller number of fixations those positions induce. Although the visitor’s level of internet experience affected his/her visual attention towards the banner, its impact on banner recall was non-significant. Results are discussed considering which variables produce greater effectiveness in capturing the user’s attention.

The paper draws several implications for the marketing literature, hospitality management and society in general.

The study is the first to analyse the impact of the position of a static ad on users’ visual attention and memory, considering the user’s degree of internet experience.

Este estudio analiza la eficacia de un banner promocional estático ubicado en un sitio web de reservas de un hotel en términos de captación de la atención visual del visitante examinando cómo este efecto depende del grado de experiencia del usuario en Internet.

Se llevó a cabo un experimento utilizando la metodología de seguimiento ocular (eye tracking), junto con un cuestionario autoadministrado. A través de la tecnología de eye tracking, se registraron los movimientos oculares mientras los participantes exploraban el sitio web genérico de un hotel. Los factores considerados en los análisis fueron la posición del banner en el sitio web y la experiencia de los participantes como usuarios de Internet.

Los hallazgos mostraron que colocar un banner en ciertas ubicaciones de la página web puede conducir a un mayor recuerdo, lo que, en parte, parece ser el resultado de la atención visual que atraen tales posiciones. El análisis de mediación mostró que las posiciones inferior-derecha e inferior-izquierda tienen un efecto negativo en el recuerdo del banner debido, en parte, a los tiempos de atención más cortos y al menor número de fijaciones que provocan esas posiciones. Aunque el nivel de experiencia de Internet del visitante afectó su atención visual hacia el banner, su impacto en el recuerdo del banner no fue significativo. Los resultados son discutidos considerando qué variables producen mayor efectividad a la hora de captar la atención del usuario.

El artículo presenta una serie de implicaciones para la literatura de marketing, la gestión hotelera y la sociedad en general.

Nuestro estudio es el primero en analizar el impacto de la posición de un anuncio estático en la atención visual y la memoria de los usuarios, considerando el nivel de experiencia del usuario en Internet.

本研究分析了位于酒店预订网站上的静态促销横幅在吸引访问者视觉注意力方面的效果, 并探索了这种效果如何受到用户的互联网体验程度的影响。

本文除了采用自填问卷的方式, 还使用眼动追踪方法进行了实验。通过眼动追踪技术, 记录了参与者在浏览一般酒店网站时的眼动情况。分析中使用的因素是网站上横幅的位置和参与者作为互联网用户的体验。

调查结果显示, 将横幅定位在网页上的某些位置可能会导致更佳的记忆, 这种效果在一定程度上是由于横幅的位置吸引了视觉注意力。调解分析表明, 横幅在右下角和左下角的位置对的回忆有负面影响, 部分原因是这些位置引起的注意时间较短、固定次数较少。虽然访问者的互联网体验水平影响了他/她对横幅的视觉注意力, 但其对横幅记忆的影响并不显著。讨论结果时考虑到了某些变量在吸引用户的注意力方面具有更明显的效果。

本文对市场营销文献、酒店管理和社会都具有一定的借鉴意义。

本次研究是第一个分析静态广告的位置对用户视觉注意力和记忆的影响的研究, 考虑到用户的互联网体验程度。

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.