A probabilistic approach for eye-tracking based process tracing in catalog browsing

Eye movements are an important cue to understand consumer decision processes. Findings from existing studies suggest that the consumer decision process consists of a few different browsing states such as screening and evaluation. This study proposes a hidden Markov-based gaze model to reveal the characteristics and temporal changes of browsing states in catalog browsing situations. Unlike previous models that employ a heuristic rule-based approach, our model learns the browsing states in a bottom-up manner. Our model employs information about how often a decision maker looks at a selected item (the item finally selected by a decision maker) to identify the browsing states. We evaluated our model using eye tracking data in digital catalog browsing and confirmed our model can split decision process into meaningful browsing states. Finally, we propose an estimation method of browsing states that does not require the information of the selected item for applications such as an interactive decision support

A two-step approach for interest estimation from gaze behavior in digital catalog browsing

While eye gaze data contain promising clues for inferring the interests of viewers of digital catalog content, viewers often dynamically switch their focus of attention. As a result, a direct application of conventional behavior analysis techniques, such as topic models, tends to be affected by items or attributes of little or no interest to the viewer. To overcome this limitation, we need to identify “when” the user compares items and to detect “which attribute types/values” reflect the user’s interest. This paper proposes a novel two-step approach to addressing these needs. Specifically, we introduce a likelihood-based short-term analysis method as the first step of the approach to simultaneously determine comparison phases of browsing and detect the attributes on which the viewer focuses, even when the attributes cannot be directly obtained from gaze points. Using probabilistic latent semantic analysis, we show that this short-term analysis step greatly improves the results of the subsequent step. The effectiveness of the framework is demonstrated in terms of the capability to extract combinations of attributes relevant to the viewer’s interest, which we call aspects, and also to estimate the interest described by these aspects

DYNAMIC CONSUMER DECISION MAKING PROCESS IN E-COMMERCE

This dissertation studies the dynamic decision making process in E-commerce. In the first essay, we use eye tracking to investigate how consumers make information acquisition decisions on attribute-by-product matrices in online choice environment such as comparison websites. Hierarchical Hidden Markov Model is used to describe this process. The model consists of three connected hierarchical layers: a lower layer that describes the eye movements, a middle layer that identifies product- and attribute-based information acquisition modes, and an upper layer that flexibly captures switching between these modes over time. Findings of a controlled experiment show that low-level properties of the eye and the visual brain play an important role in dynamic information acquisition. Consumer switch frequently between two acquisition modes, and higher switching frequency increases decision time and reduces easiness of decision making. These results have implications for web design and online retailing, and may open new directions for research and theories of online choice. The second essay investigates how usage experience with different types of decision aids contributes to the evolution of online shopping behavior over time. In the context of online grocery stores, we categorize four types of decision aids that are commonly available, namely, those 1) for nutritional needs, 2) for brand preference, 3) for economic needs, and 4) personalized shopping lists. We construct a Non-homogeneous Hidden Markov Model of category purchase incidence and purchase quantity, in which parameters are allowed to vary over time across hidden states as driven by usage experience with different decision aids. The dataset was collected during the period when the retailer first launched its web business, which makes it particularly suited to study the evolution of online purchase behavior. We estimate the model for the spaghetti sauce and liquid detergent categories. Results indicate that four types of decisions influence evolution of purchase behavior differently. Findings from this study enrich the understanding of how purchase behavior may evolve over time in online stores, and provide valuable insights for online retailers to improvement the design of their store environments

IDENTIFYING PREFERENCES THROUGH MOUSE CURSOR MOVEMENTS – PRELIMINARY EVIDENCE

Identifying customers’ preferences is a challenging task with significant practical implications for online shopping. Current methods often put considerable burden on the customers through such methods as questioning, so the process could benefit from a more accurate and less intrusive estimation of how customers weight product attributes, particularly in the initial purchasing phase. Our goal is to derive attribute weights automatically by recording and analyzing cursor movements. We conducted an experiment to confirm the suitability of the proposed design, and found a highly significant correlation between the time people spend investigating a product attribute and their self-reported importance rating. Our proposed Web page design might also reduce the risk of information overload

Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Many data sets, from different and seemingly unrelated marketing domains, all involve paths—records of consumers\u27 movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers\u27 motivations and behaviors, path data sets will become more common and will play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of a path (in a marketing context), and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying subdimensions. Based on this framework, we cover a range of important operational issues that should be taken into account as researchers begin to build formal models of path-related phenomena. We close with a brief look into the future of path-based models, and a call for researchers to address some of these emerging issues

Consumer Perception of Online Advertising - The Effects of Animation, Ad Characteristics, Repetition and Task Relevancy on Attention and Memory

Prior advertising research on advertising perception models has mainly focused on effects that occur after consumers have been exposed to advertising stimuli. Little research has examined how consumers are exposed to advertising and the quality of visual attention during advertising exposure. This doctoral dissertation examines how consumers allocate their visual attention to online ads and how consumers memorize ads in different viewing conditions. More precisely, the dissertation focuses on how ad format and location, animation, repetition, abrupt onsets, and task relevancy affect attention to ads and memory performance. The thesis employs theories of cognitive psychology, visual marketing and consumer behavior, advertising persuasion models and computer science and applies experimental methodologies such as eye tracking besides consumers' self-reported measures. The thesis consists of four essays. Essay 1 introduces a review of relevant theory and eye tracking methodology for online advertising research. The next three essays present experimental studies. Essay 2 investigates the effects of ad format and animation on attention and memory. Essay 3 examines the effects of repetition of ads on memory. Essay 4 investigates how animation, ad format and abrupt onsets of ads affect reading performance, and how online ads are perceived during free browsing compared to an instructed reading task. Our findings indicate that attention and memory for ads were significantly affected by consumers' intentions, ad characteristics and web page contents. Consumers are more likely to be attracted by ads when browsing web sites freely without a special task. Ad characteristics, such as animation and ad format interact and influence differently on attention and memory performance for ads depending on the ad's location on a page and the surrounding page content. The thesis also tested the effects of repetition of ads as a potential strategy to improve memory for ads. A significant positive effect was found already at rather low levels of repetition. Moreover, we also tested consumers' attention to abrupt onsets of ads. We registered a significant increase of attention to abrupt onsets of ads as compared with permanent ads especially during free browsing of web pages. This thesis increases our knowledge of the role and type of ad exposure on consumers' attention by evaluating the effectiveness of advertising exposure in dynamic online environment. This research is also the first attempt to evaluate the applicability of the primary eye tracking measures for online advertising. For advertisers, media traders and graphic designers this research proposes new strategies about how to adjust ad format and placement, animation and repetition to break through advertising clutter and reduce consumers' ad avoidance to develop stronger brand awareness and preferences

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

¿Qué dicen sus ojos? Conectando los movimientos oculares hacia el comportamiento del consumidor

Eye tracking (ET) is a technique that has been progressively employed to study the influence of visual stimuli on attentional processes and consumer behavior. The goals of the present theoretical article are fourfold and are based on an extensive literature revision. First, a brief historical review of ET methodology is introduced, presenting the evolution of ET techniques from the ancient proto-eye trackers to the "fresh" state-of-the-art eye ET devices. Second, the basics of ET are clarified through a simplified technical and mathematical explanation. Third, the triad eye movement-attention-consumer behavior is made clear, grounded on attention, interest, desire, and action (AIDA) theoretical model. Fourth, the most used oculometrics in marketing studies are explained and distinguished The present article addresses a number of technical and methodological issues by discussing challenges involved in ET systems and giving some guidelines for those who intend to apply ET to infer cognitive and emotional processes.info:eu-repo/semantics/publishedVersio

Essays On Dynamic Updating Of Consumer Preferences

Consumers dynamically update their preferences over time based on information learned through product search and consumption experiences, particularly in online media. Using three unique datasets from different domains, we address specific ways in which firms can use rich information about their customers\u27 behaviors to improve (1) the visual display of products on a webpage in online shopping, (2) predictions of new product adoption in online gaming, and (3) the timing of product release in online learning. First, we explore how consumers visually search through product options using eye-tracking data from two experiments conducted on the websites of two online clothing stores, which can inform retailers on how to position products on a virtual webpage. Second, we examine how consumers\u27 variety-seeking preferences change depending on past consumption outcomes within the context of an online multi-player video game, which can be used to improve predictions of new product adoption. Third, we use clickstream data from an online education platform to test theories of goal progress, knowledge accumulation, and boundedly rational forward-looking behavior, which can be used to explain binge consumption patterns and inform content providers on the best way to structure and release content. In each of these three projects, we build a mathematical model of individual decisions, with the parameterization grounded in theories of consumer behavior, and we demonstrate through in-sample prediction that our model is able to capture specific heterogeneous patterns within the data. We then test that our model is able to make out-of-sample predictions related to managerial interventions, and empirically verify our predictions using either lab experiments or new field data following a natural experiment policy change