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Attention web designers: You have 50 milliseconds to make a good first impression! Behaviour and Information Technology, 25(2), 115-126
Abstract
Three studies were conducted to ascertain how quickly people form an opinion about web page visual appeal. In the first study, participants twice rated the visual appeal of web homepages presented for 500 ms each. The second study replicated the first, but participants also rated each web page on seven specific design dimensions. Visual appeal was found to be closely related to most of these. Study 3 again replicated the 500 ms condition as well as adding a 50 ms condition using the same stimuli to determine whether the first impression may be interpreted as a 'mere exposure effect' (Zajonc 1980). Throughout, visual appeal ratings were highly correlated from one phase to the next as were the correlations between the 50 ms and 500 ms conditions. Thus, visual appeal can be assessed within 50 ms, suggesting that web designers have about 50 ms to make a good first impression.
... Visual complexity refers to the level of visual variation in a design. Such variation can influence the attention given to, attractiveness of, and attitudes toward information (Lindgaard et al., 2006;Pieters et al., 2010). Visual complexity can affect users' online behavior and experience and can be measured using visual factors, such as the information hierarchy, font-weight, and color richness of a layout (Tuch et al., 2009). ...
... Based on the principles and practices of visual design, the evaluation of visual hierarchy is achieved through the arrangement of columns and rows in the page layout, coupled with the manipulation of color and font weight or size (Couper et al., 2013;Kuba and Jeong, 2023;Muller-Brockmann, 1985;Retore et al., 2016). Although research has investigated the influence of visual complexity on attitudes and has employed numerous definitions and measurement standards (Lindgaard et al., 2006;McQuarrie and Mick, 1996;Peracchio and Meyers-Levy, 1994;Pieters et al., 2010), the role of complexity in health information design has yet to be fully elucidated. Therefore, the present study analyzed effective design and communication of health information as well as assessed health information recipients' comprehension, attitudes, and behavioral intentions. ...
... Visual complexity and information architecture are critical factors in representing online health information. These factors shape initial impressions of online health messages, serve as predictive indicators for future use intentions, and significantly influence users' evaluations of the information over time (Lavie and Tractinsky, 2004;Lindgaard et al., 2006;Tuch et al., 2012). Persuasive designs have an appropriate level of complexity and effectively represent target concepts. ...
Social media is a valuable tool that enables public health organizations to communicate effectively. To enhance the reach of health communication on social media, scholars have proposed that emoji be used to convey scientific information. The current study explored the influence of emoji on the effectiveness of health communication on social media. Automated content analysis revealed that the presence of emoji in online health information resulted in higher levels of social media engagement (SME) than the absence of emoji did. Additionally, a 2 (emoji: present versus absent) × 3 (visual complexity of information design: low versus medium versus high) online experiments revealed that the presence of emoji in health information sequentially increased perceived enjoyment and perceived interactivity, thereby promoting SME. However, this effect is influenced by the visual complexity of health information designs. The presence of emoji is only effective in increasing SME with health information presented using a design with low or medium visual complexity. This study provides theoretical and practical insights into visual health communication and health information design.
... On the shorter end of the spectrum, a good-vs-bad first impression of a web page forms very quickly. A 50 ms interval was found to be enough for users to decide whether they like a design or not (Lindgaard et al. 2006)results being highly similar to 500 ms. Tuch et al. (2012) show that the factors of visual complexity and protypicality influence perceived beauty of web pages already within 17 ms. ...
... Feedback in the 2second ET group is the most minimalistic and identifies the fewest elements and characteristics. Lack of differences in attitudinal questions is corroborated by Lindgaard et al. (2006) attitudes form very quickly, so for testing visual appeal by itself, 2 seconds are enough time. ...
... La seconde est l'ensemble des facteurs de design qui font référence à l'architecture du site et qui conditionnent la navigabilité et les conditions d'accès à l'offre recherchée (types de menus, de rubriques, de commandes de contrôle). Et la troisième réunie les facteurs sociaux que l'on peut définir comme étant l'ensemble des outils permettant aux internautes d'interagir entre eux ou avec l'entreprise (chatbots, avis clients, forums de discussion) (Lemoine, 2012 (Lindgaard, 2006). ...
Evaluating information quality online is increasingly important for healthy decision‐making. People assess information quality using visual interfaces (e.g., computers, smartphones) with visual cues like aesthetics. Yet, voice interfaces lack critical visual cues for evaluating information because there is often no visual display. Without ways to assess voice‐based information quality, people may overly trust or misinterpret information which can be challenging in high‐risk or sensitive contexts. This paper investigates voice information uncertainty in one high‐risk context—health information seeking. We recruited 30 adults (ages 18–84) in the United States to participate in scenario‐based interviews about health topics. Our findings provide evidence of information uncertainty expectations with voice assistants, voice search preferences, and the audio cues they use to assess information quality. We contribute a nuanced discussion of how to inform more critical information ecosystems with voice technologies and propose ways to design audio cues to help people more quickly assess content quality.
The research on decision-making driven by product rankings faces challenges due to the rise of extensive positive reviews and the widespread distribution of electronic word of mouth (eWOM) across multiple platforms. There is a limited body of research that examines the impact of platform credibility on the quality of product rankings. Hence, based on the Basic Uncertain Information (BULI), which enables simultaneous representation of information and its credibility, we investigate the development of a ratings aggregation approach for cross-platform distribution (CPD) with the aim of facilitating decision-making processes, focusing specifically on the aspect of credibility. To begin with, this paper introduces the concept of BULI as a means to effectively represent both product ratings and their corresponding levels of credibility. Subsequently, we proceeded to devise the BULI-based aggregation functions that are well-suited for the aggregation of CPD ratings and that can be degraded to the existing operator. In addition, we develop a credibility evaluation index system and credibility calculation model for the platform in order to derive a product BULI matrix consisting of ratings and their corresponding levels of credibility. In this study, we propose two models, namely the feature information-based user weighting model and the BULI distance measure-based Technique for Order Preference by Similarity to an Ideal Solution (BULI-TOPSIS) model, to enhance the product ratings aggregation approach for decision-making purposes. The utilization of the proposed method is exemplified through the case study of passenger car ranking, showcasing its practicality and efficiency.
Online health consultation (OHC) platforms have been extensively used since the outbreak of COVID-19, that the number of active users has been increasing dramatically in China. OHC interfaces require a more immersive environment to support the entire medical consultation journey. This study intends to explore the design features of existing OHC platforms, and how they relate to patient experience and patient OHC journey. A literature review of design features, patient experience, and patient OHC journey was conducted to generate a framework to guide the study. Then, semi-structured interviews with 10 participants were carried out. The notions of “Design Features” were interpreted differently by individuals. For example, ‘graphic design’ was viewed as colour, font, icon, etc. Design Features have varying degrees of influence on “Patient Experience” and “Patient OHC Journey”. This study explores what comprises Design Features of OHC platforms. The associations between Design Features and Patient Experience found in this study are different from the associations that have been found in other contexts, like e-commerce. The design on telemedicine platforms especially OHC should be studied as an independent topic to inform the design of a better patient experience throughout the OHC journey.
Purpose
This research aims to analyze the role of visuals posted on the social media of NGOs and to predict the popularity of a post based on the characteristics of the visual it contains.
Design/methodology/approach
Two social media platforms, namely Facebook and Instagram, were selected as the empirical study environments. Specifically, all visuals posted on 12 child-related Non-Government Organizations during the period of 2020–2021 (4,144 in total) were collected and subsequently subjected to automatic characterization using visual recognition and artificial intelligence tools. Machine learning algorithms were then employed to predict the popularity of a post solely based on the visuals it contains, as well as to identify the most significant features that serve as predictors for post popularity.
Findings
The Support Vector Classifier performed best with a prediction accuracy of 0.62 on Facebook and 0.81 on Instagram. For the explanation of the model, we used feature importance metrics and found that features like the presence of people and the emotions of joy and calmness are important for the prediction.
Practical implications
Companies and organizations serve a large part of their communication strategy through social media. Given that every advertiser would like to use their funds in the most efficient way, the ability to predict the performance of a post would be a very important tool.
Social implications
The methodology can be used in the non-profit sector, whereby knowing what visual will perform better they could promote their mission more effectively, increase public awareness, raise funds and reduce expenses on their communication strategy.
Originality/value
The novelty of this work regarding popularity prediction on social media lies in the fact that to make the prediction, it focused exclusively on the visual and its characteristics and achieved high accuracy scores in the case of Instagram. Additionally, it provided important information about visual characteristics and their importance in predicting popularity.
This paper presents the results of the investigation on the impact of images on both the quality of news content and its engagement, with a focus on both highbrow and tabloid news organizations. Since visual media are easier to remember than words and can attract the readers’ attention, they play a significant role in what content online readers decide to consume. The aim of this study is to identify the defining characteristics of images that accompany high-quality news and determine whether it is possible to classify news articles based on images. This work is part of a broader project that utilizes natural language processing techniques to predict news quality. To extract the features, several image recognition tools have been used, and different supervised machine learning models have been tested to predict the quality of digital news stories. To shed light on the results explainable AI methods were employed. The findings of this study offer new insights into the image features that contribute to high-quality news and audience engagement on social media.
An evolved module for fear elicitation and fear learning with 4 characteristics is proposed. (a) The fear module is preferentially activated in aversive contexts by stimuli that are fear relevant in an evolutionary perspective. (b) Its activation to such stimuli is automatic. (c) It is relatively impenetrable to cognitive control. (d) It originates in a dedicated neural circuitry, centered on the amygdala. Evidence supporting these propositions is reviewed from conditioning studies, both in humans and in monkeys; illusory correlation studies; studies using unreportable stimuli; and studies from animal neuroscience. The fear module is assumed to mediate an emotional level of fear learning that is relatively independent and dissociable from cognitive learning of stimulus relationships.
The role of response transformations was examined in an information integration task in which different models were supported for different response scales. In Experiment 1, one group of subjects made subjective estimates of cost in dollars (cost estimates) for a series of hypothetical trips described by varying levels of gasoline price, gas mileage, and distance and another group of subjects made comparative ratings of the relative expense of each trip. A multiplicative model was supported for the cost estimates and an additive model was supported for the comparative ratings. In Experiment 2, a single group of subjects made cost estimates and then were asked, in the absence of stimulus values, to convert these judgments to comparative ratings. A multiplicative model was supported for each response mode, indicating a linear response transformation. It was concluded that the different response patterns in Experiment 1 were due to different integration processes for different response requirements rather than a nonlinear response transformation.
This landmark volume brings together the work of the world's leading researchers in subliminal perception. This compilation marks a fundamental shift in the current study of subliminal effects: No longer in question is the notion that perception without awareness occurs. Now, the emphasis is on elucidating the parameters of subliminal effects and understanding the conditions under which stimuli perceived without awareness significantly influence affect, cognition, and behavior.
"Perception Without Awareness" firmly establishes subliminal perception within the mainstream of psychological science. Well represented here are the two main research branches that have emerged: One directly investigates the nature of subliminal effects; the other uses subliminal techniques as tools for investigating psychological phenomena such as hypnosis, dreaming, repression, social judgment and inference, psychopathology, and symptom formation.
Timely and thought-provoking, "Perception Without Awareness" is sure to be of enormous interest to all psychoanalytic clinicians and scholars, as well as cognitive, clinical, and social psychologists whose work touches upon issues relating to psychopathology, perception, cognition, and memory. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Psychophysical scaling models of the form R = f(I), with R the response and I some intensity of an attribute, all assume that people judge the amounts of an attribute. With simple biases excepted, most also assume that judgments are independent of space, time, and features of the situation other than the one being judged. Many data support these ideas: Magnitude estimations of brightness (R) increase with luminance (I). Nevertheless, I argue that the general model is wrong. The stabilized retinal image literature shows that nothing is seen if light does not change over time. The classification literature shows that dimensions often combine to produce emergent properties that cannot be described by the elements in the stimulus. These and other effects cannot be adjusted for by simply adding variables to the general model because some factors do not combine linearly. The proposed alternative is that people initially judge the entire stimulus – the object in terms of its environment. This agrees with the constancy literature that shows that objects and their attributes are identified through their relations to other aspects of the scene. That the environment determines judgments is masked in scaling studies where the standard procedure is to hold context constant. In a typical brightness study (where different lights are presented on the same background on different trials) the essential stimulus might be the intensity of the light or a difference between the light and the background. The two are perfectly confounded. This issue is examined in the case of audition. Judgments of the loudness of a tone depend on how much that tone differs from the previous tone in both pitch and loudness. To judge loudness (and other attributes) people first seem to process the stimulus object in terms of differences between it and other aspects in the situation; only then do they assess the feature of interest. Psychophysical judgments will therefore be better interpreted by theories of attention that are based in biology or psychology than those (following Fechner) that are based in classical physics.
Human Factors (HF) has traditionally concerned itself with usability, effectiveness and efficiency without regard for the impact that the ‘look and feel’ of products and services might have on human performance. Recent research shows clearly that aesthetics, a ubiquitous, powerful function that permeates the design of products and services, matters. Within a consumer-driven industrial society it is ignored at the manufacturer's or service provider's peril. In order to encourage HF researchers to begin thinking about aesthetics in design, this paper attempts to position aesthetics within an evolutionary context, and to provide both a psychological framework and physiological underpinnings. Whitfield's Collative-Motivation models of aesthetics is outlined to account for results from much of the experimental research on preferences. Barnard's Interacting Cognitive Sub-systems (ICS) architecture is discussed in some detail, as, contrary to most other cognitive frameworks, it allows smooth integration of cognition and emotion. Physiological processes involved in emotional responses are discussed and the ICS framework is applied to explain both these results and findings suggesting that ‘emotion precedes cognition’. Finally, the integration of aesthetics and the Collative-Motivation model within the ICS framework is attempted.