Shopping alone online vs. co-browsing: a physiological and perceptual comparison

Although shopping is a social activity frequently performed with friends and family members, most online shopping is done alone. With the development of Web 2.0 technologies and the increasing popularity of social networking sites, online social shopping has emerged as a new phenomenon that allows more social interaction, participation, and satisfaction for customers while shopping online. Therefore, companies have started to use social shopping tools in their e-commerce websites to facilitate online social shopping. Co-browsing is one of the more recent online social shopping tools available, enabling users to shop or browse together by offering synchronized web views and chat facilities. Prior research in co-browsing focused primarily on the technical and design aspects of co-browsing. More needs to be done to understand the behavioral, emotional, and social aspect of co-browsing. In this study, we focus on the social aspect of co-browsing and explore the following research questions: (1) How does co-browsing affect consumers\u27 cognitive beliefs, emotions, and behaviors? (2) How is co-browsing different than shopping alone online? To address these questions, an experimental study is performed, which includes shopping alone and shopping with a companion by using a co-browsing tool. By recording and analyzing physiological responses such as eye gaze and skin conductance, we are able to gain better insight into how individuals react--both physically and perceptually--to co-browsing during an online shopping task --Abstract, page ii

Incorporating Cognitive Neuroscience Techniques to Enhance User Experience Research Practices

User Experience (UX) involves every interaction that customers have with products, and it plays a crucial role in determining the success of a product in the market. While there are numerous methods available in literature for assessing UX, they often overlook the emotional aspect of the user\u27s experience. As a result, cognitive neuroscience methods are gaining popularity, but they have certain limitations such as difficulty in collecting neurophysiological data, potential for errors, and lengthy procedures. This article aims to examine the most effective research practices using cognitive neuroscience techniques and develop a standardized procedure for conducting UX research. To achieve this objective, the study conducts a comprehensive review of UX research that employs cognitive neuroscience methods published between 2017 and 2022

Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities

Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the signals are highly sensitive, and many controls are required in laboratory user studies. To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). Meanwhile, we collected data on blood volume pulse, electrodermal activity, and pupil responses. We then used machine learning approaches as a mechanism to examine the influence of controlled and uncontrolled variables that commonly arise in user studies. Task duration was found to have a substantial effect on the model performance, suggesting it represents individual differences rather than giving insight into the target variables. This work contributes to our understanding of such variables in using physiological signals in information retrieval user studies.Comment: Accepted to the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23

Engaged or Frustrated? Disambiguating Engagement and Frustration in Search

One of the primary ways researchers have characterized engagement is by an increase in search actions. Another possibility is that instead of experiencing increased engagement, people who click and query frequently are actually frustrated; several studies have shown that frustration is also characterized by increases in clicking and querying behaviors. This research seeks to illuminate the differences in search behavior between participants who are engaged and frustrated, as well as investigate the effect of task interest on engagement and frustration. To accomplish this, a laboratory experiment was conducted with 40 participants. Participants completed four tasks, and responded to questionnaires that measured their engagement, frustration, and stress. Participants were asked to rank eight topics based on interest, and were given their two most interesting and two least interesting tasks. Poor search result quality was introduced to induce frustration during their most interesting and least interesting tasks. This study found that physiological signals hold some promise for disambiguating engagement and frustration, but this depends on the time frame and manner in which they are examined. Frustrated participants had significantly more skin conductance responses during the task, while engaged participants had greater increases in skin conductance during the first 60 seconds of the task. Significant main and interaction effects for interest and frustration were found for heart rate in the window analysis, indicating that heart rate fluctuations over time can be most effective in distinguishing engagement from frustration. The multilevel modeling of engagement and frustration confirmed this, showing that interest contributed significantly to the model of skin conductance, while frustration contributed significantly to the model of heart rate. This study also found that interest had a significant effect on engagement, while the frustrator effectively created frustration. Frustration also had a significant effect on self-reported stress. Participants exhibited increases in search actions such as clicks and scrolls during periods of both engagement and frustration, but a regression analyses showed that scrolls, clicks on documents, and SERP clicks were most predictive of a frustrating episode. A significant main effect for interest was found for time between queries, indicating that this could be a useful signal of engagement. A model including the physiological signals and search behaviors showed that physiological signals aided in the prediction of engagement and frustration. Findings of this research have provided insight into the utility of physiological signals in distinguishing emotional states as well as provided evidence about the relationship among search actions, engagement and frustration. These findings have also increased our understanding of the role emotions play in search behavior and how information about a searcher’s emotional state can be used to improve the search experience.Doctor of Philosoph

Towards Consumer 4.0 Insights and Opportunities under the Marketing 4.0 Scenario

This Research Topic is a sequel to our previous Research Topic “From Consumer Experience to Affective Loyalty: Challenges and Prospects in the Psychology of Consumer Behavior 3.0”. This first article collection was devoted to analyze the changes that appeared in different industries and companies, fostered by factors mainly related to the development of technologies. The evolution from consumer 3.0 to consumer 4.0 represents an opportunity to feature the changes that have been occurring lately as well as to gain an insight into the future of consumer behavior. Nowadays, the markets are experiencing several transformations in consumer behavior. These changes have been fueled by several trends: processes of globalization that produced an extraordinary assortment of diverse products and brand alternatives, new business models based on the intensive use of technology advances in communication and mobile technologies that allow customers’ capacity to easily participating in co-creation processes with companies; and big data developments. In this scenario, customers acquired more power than ever before due to their availability of information required to choose among the better priced alternatives product-brand options, as well as the technological means to access to such alternatives. Thus, customers evolved from a position to simply receiving the offer proposed by companies, to a position of power where they had the last word in the decision process, that is, the position of consumer 3.0. These consumers were characterized by their ability to adopt and use new technologies to meet their individual needs. What is more, these types of consumers did not longer easily respond to traditional mass marketing techniques. Instead, this generation of consumers demanded a highly customized approach across all facets of businesses including new product development, communication and customer service, among others. Nevertheless, in the advent of Marketing 4.0, a new type of consumer is observed, namely the customer 4.0. The transition from consumer 3.0 to consumer 4.0 is becoming evident, not only in consumers’ behavior but also in companies’ behavior. Related to the first one, consumers 4.0 are hyper-connected through different technologies, including not only the well-known mobile or digital technologies, but also other type of technologies, such as IoT, nanotech or artificial intelligence. Hence, their behavior is characterized by the demand of technology that have integrated the facets of Marketing 4.0 such as geolocation, marketing virtual and augmented reality facets. Regarding the second one, companies should face a digital transformation affecting not only value areas, but also, the way business interact with the environment. In particular, companies need to incorporate systems and applications that allow them to collect and analyze information, while helping decision making, since in the long run these issues constitute the cornerstone on which to start building a successful marketing strategy 4.0. This Research Topic welcomes scientific papers that covers the following topics (but not limited exclusively): - Consumers’ 4.0. behavior in different countries, industries, products, brands, etc.; - Digital transformations of industries and companies due to new consumption patterns; - New devices launched by companies work to meet the demands of consumer 4.0 (e.g., IoT), as well as the use consumers make of such devices; - The latest technology trends in business areas that make easier the consumer-companies relationships (processing, communication or any other digital technologies)

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Mobile advertising effectiveness versus PC and TV using consumer neuroscience

This Doctoral Thesis, entitled Mobile Advertising Effectiveness versus PC and TV, Using Consumer Neuroscience, while analyzes both the evolution of mobile advertising and its current situation, also discusses, how effective is mobile advertising when compared against advertising in other digital devices, such as PC and TV. The last few years have been characterized by an increase of the time that consumers spend on their mobile phones and as a result, by an increase in the expending on digital mobile advertising. Brands are already demanding models that measure digital advertising effectiveness, and consumer neuroscience technology may help, not only to measure it, but also to understand its impact on consumers. Considering this environment, this research proposes various recommendations for advertisers that may be considering using consumer neuroscience technology to measure mobile advertising effectiveness, as well as recommendations on how to design mobile ads that increase advertising effectiveness