Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness

Users make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.Engineering and Applied Science

Predicting the Mobile Consumer Purchase Behavior using Quantified Visual Preferences

Most mobile consumers make a decision about the product in a split second. The decision making in the mobile environment is surely faster than in front of the desktop. This paper claims that the decision-making in the mobile shopping is highly depending on the product’s first impression and their visual preference. By predicting the human’s visual preference based on the image processing model of perceived colorfulness and perceived visual complexity, this study tested an S-O-R path model from visual preference to consumer’s bookmarking and purchase intention via age and gender as moderators. With the controlled laboratory experiment, we substantiated our predicting image preference model. Further, a plan for a real data based analysis is proposed to validate the congruity of our model with the Korean mobile shopping company later

Predictive modeling of webpage aesthetics

Aesthetics plays a key role in web design. However, most websites have been developed based on designers\u27 inspirations or preferences. While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In recent years, machine learning methods have shown promising results in image aesthetic assessment. In this research, we used machine learning methods to study and explore the underlying principles of webpage aesthetics --Abstract, page iii

User's web page aesthetics opinion: a matter of low-level image descriptors based on MPEG-7

Analyzing a user's first impression of a Web site is essential for interface designers, as it is tightly related to their overall opinion of a site. In fact, this early evaluation affects user navigation behavior. Perceived usability and user interest (e.g., revisiting and recommending the site) are parameters influenced by first opinions. Thus, predicting the latter when creating a Web site is vital to ensure users’ acceptance. In this regard, Web aesthetics is one of the most influential factors in this early perception. We propose the use of low-level image parameters for modeling Web aesthetics in an objective manner, which is an innovative research field. Our model, obtained by applying a stepwise multiple regression algorithm, infers a user's first impression by analyzing three different visual characteristics of Web site screenshots—texture, luminance, and color—which are directly derived from MPEG-7 descriptors. The results obtained over three wide Web site datasets (composed by 415, 42, and 6 Web sites, respectively) reveal a high correlation between low-level parameters and the users’ evaluation, thus allowing a more precise and objective prediction of users’ opinion than previous models that are based on other image characteristics with fewer predictors. Therefore, our model is meant to support a rapid assessment of Web sites in early stages of the design process to maximize the likelihood of the users’ final approval

Image Analysis to Assess the Impact of Photo Aesthetics on Online Consumer Click-through: An Empirical Study

Determinants of consumer’s shopping behavior are of long-term interest to researchers. Since product photos directly aid consumers’ understanding of products, retailers often put a lot of effort into polishing them. However, there is limited research on the impact of product photos on shopping behavior. This research takes advantage of image-processing techniques to study product photos’ impact. These techniques allow us to investigate a large set of photo characteristics simultaneously in an empirical study. To rule out possible confounding factors, we use a real company dataset from a social shopping Website, which has a simple interface allowing consumers to judge products mainly based on their photos. We employ two-stage nested logit model embedded with differences-in-differences approach and examine product photo characteristics from the aspects of color, composition, complexity, and model face. We found that consumers prefer to click product photos with a warmer color, a larger key object, appropriate complexity

The Influence of User Interface Attributes on Aesthetics

In this paper we present an empirical study among 40 participants which investigates the relationship between various factors of user interface aesthetics on the one hand, and the influence of the user interface attributes, symmetry, colorfulness as well as visual complexity on user interface aesthetics on the other hand. The user interface aesthetics will be classified in intuitive aesthetics (1st impression with a presentation time of 500 ms) and reflective aesthetics (reflective long-term impression after a longer presentation). Reflective aesthetics is further classified in classical aesthetics (common attractiveness) as well as expressive aesthetics (creativity). For this study we have set up a corpus of 30 websites which are used as stimulus material. In a multi-step lab experiment, participants rate aesthetics and their subjective impression concerning user interface attributes using questionnaires. We are able to show that the intuitive aesthetic judgment correlates strongly with the reflective judgment. The symmetry of a website positively correlates with all definitions of aesthetics, especially with the classical or traditional interpretation in the sense of attractiveness. Visual complexity can be seen as the strongest predictor for the aesthetic judgement of users and it negatively correlates with all definitions. Concerning colorfulness, a preference for websites of a medium degree of colorfulness for the intuitive as well as the classical aesthetics can be stated. Concerning expressive aesthetics, websites of moderate to high colorfulness receive the best judgments. The relationships which we have found are finally discussed in the context of previous research and some implications for future user interface design are given

SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

The effect of multi-device design on website efficiency and user preference

Modern websites must accommodate many different devices with varying screen size without decreasing user experience or losing relevant features or content. In this thesis, three different multi-device design approaches, adaptive, responsive and mobile-dedicated, were researched on desktop, tablet and smartphone devices to ascertain whether one approach is superior to the others in terms of user preference and website simplicity and efficiency. A total of eight mock websites were created to represent the approaches on each device. The mock websites were first evaluated with an expert analysis, wherein the indi-vidual page load times and aesthetic values of the sites were calculated. Then, a user study was performed, where 10 participants performed search tasks on each mock website, evaluating each site after completing the tasks. Additionally, a semi-structured interview was conducted after each study session and eye tracking data was collected during the study to identify possible differences in gaze behavior between the mock sites. The results showed that no single approach was superior, as the results were very similar. However, it was discovered that participants disliked the mobile layout on the desktop device, even though it produced the highest efficiency. The results additionally suggested that mobile devices are preferred for their ease of use and accessibility, in-stead of for the layout design. Finally, a behavior where participants let their eyes rest while using the device to browse through a site was observed. Further study is suggested for the behavior, dubbed restful browsing, as it could have a strong influence on mobile web use