Towards an Integrated Clickstream Data Analysis Framework for Understanding Web Users’ Information Behavior

Clickstream data offers an unobtrusive data source for understanding web users’ information behavior beyond searching. However, it remains underutilized due to the lack of structured analysis procedures. This paper provides an integrated framework for information scientists to employ in their exploitation of clickstream data, which could contribute to more comprehensive research on users’information behavior. Our proposed framework consists of two major components, i.e., data preparation and data investigation. Data preparation is the process of collecting, cleaning, parsing, and coding data, whereas data investigation includes examining data at three different granularity levels, namely, footprint, movement, and pathway. To clearly present our data analysis process with the analysis framework, we draw examples from an empirical analysis of clickstream data of OPAC users’ behavior. Overall, this integrated analysis framework is designed to be independent of any specific research settings so that it can be easily adopted by future researchers for their own clickstream datasets and research questions

UNDERSTANDING CONSUMERS' ONLINE INFORMATION RETRIEVAL AND SEARCH: IMPLICATIONS FOR FIRM STRATEGIES

The growth of the Internet and other digitization technologies has enabled the unbundling of the physical and information components of the value chain and has led to an explosion of information made available to consumers. Understanding the implications of this new informational landscape for theory and practice is one of the key objectives of my research. My dissertation seeks to understand how firms can use their knowledge of online consumer search and information seeking behaviors to design optimal information provision strategies. The main premise is that consumers' online search behaviors are key to understanding consumers' underlying information needs and preferences. In my first essay I specifically focus on big-ticket, high-involvement goods for which firms essentially have sparse information on their potential buyers - making information reflected in consumers' online search very valuable to online retailers. I use a new and rich source of clickstream data obtained from a leading clicks-and-mortar retailer to model consumers' purchase outcomes as a function of the product and price information provided by the retailer, and find interesting differences for sessions belonging to customers classified as browsers, directed shoppers and deliberating researchers. Since consumers typically straddle online as well as traditional channels, the second essay in my dissertation examines how online information acquired by consumers affects their choices in offline used-good markets. Secondary markets characterized by information asymmetries have typically resorted to quality-signaling mechanisms such as certification to help reduce the associated frictions. However, the value of traditional quality signals to consumers depends crucially on the extent of the asymmetries in these markets. The online information available to consumers today may help bridge such asymmetries. Drawing upon a unique and extensive dataset of over 12,000 consumers who purchased used vehicles, I examine the impact of their information acquisition from online intermediaries on their choice of (reliance on) one such quality signal - certification, as well as the price paid. These findings will help firms to better understand how the provision of different types of online information impacts consumers' choices and outcomes, and therefore help them in designing better and targeted strategies to interact with consumers

An exploration of student performance, utilization, and attitude to the use of a controlled content sequencing web based learning environment

Universities, traditionally places of teaching and research, have seen and are continuing to see radical changes occur in the area of teaching and the methods of teaching delivery. The World Wide Web, or ‘Web’ has begun to subsume the classroom as the preferred means by which students access their tertiary learning materials, and ultimately, how academic staff deliver those materials. The delivery of learning via the Web takes many forms and is generically, and usually inaccurately, referred to by such names as e-learning, online learning, web-based training and web based education, using such technologies as virtual learning environments, learning management systems and learning content management systems. This study focuses specifically on the delivery of electronic learning materials in the support of both inclass and online teaching

Proposal of a web site engagement scale and research model. Analysis of the influence of intra web site comparative behaviour

A Web site engagement scale is suggested that serves as the basis of a two part-model. The first part studies the influence of the online comparative behaviour of consumers on Web site engagement using data obtained from respondents that selected a holiday package on an online travel agency capable of remotely tracing and recording their intra-Web page and intra-Web site behaviour. The second part of the model studies the influence of Web site engagement on consequences highly relevant for online marketers. The results confirm that the Web site engagement construct has five dimensions: positive affect, focused attention, challenge, curiosity and involvement. Likewise antecedents and consequences of Web site engagement are confirmed. The model is estimated with partial least squares path modelling (PLSPM).En esta investigación se propone una escala de “enganche con sitios Web” que sirve de base para un modelo con dos partes. En la primera parte se estudia la influencia del comportamiento comparativo online de consumidores utilizando datos obtenidos a partir de encuestados que escogieron un paquete vacacional en una agencia de viajes online capaz de registrar remotamente el comportamiento intra-página Web e intra-sitio Web. En la segunda parte del modelo se estudia la influencia del constructo enganche con sitios Web sobre consecuencias de relevancia para online marketers. Los resultados confirman que el constructo enganche con sitios Web tiene cinco dimensiones: afecto positivo, atención centrada, curiosidad, implicación y reto. Asimismo se confirman antecedentes y consecuencias de este constructo. La metodología de estimación se basa sobre modelización estructural con partial least squares path modelling (PLSPM)

Memory-based preferential choice in large option spaces

Whether adding songs to a playlist or groceries to a shopping basket, everyday decisions often require us to choose between an innumerable set of options. Laboratory studies of preferential choice have made considerable progress in describing how people navigate fixed sets of options. Yet, questions remain about how well this generalises to more complex, everyday choices. In this thesis, I ask how people navigate large option spaces, focusing particularly on how long-term memory supports decisions. In the first project, I explore how large option spaces are structured in the mind. A topic model trained on the purchasing patterns of consumers uncovered an intuitive set of themes that centred primarily around goals (e.g., tomatoes go well in a salad), suggesting that representations are geared to support action. In the second project, I explore how such representations are queried during memory-based decisions, where options must be retrieved from memory. Using a large dataset of over 100,000 online grocery shops, results revealed that consumers query multiple systems of associative memory when determining what choose next. Attending to certain knowledge sources, as estimated by a cognitive model, predicted important retrieval errors, such as the propensity to forget or add unwanted products. In the final project, I ask how preferences could be learned and represented in large option spaces, where most options are untried. A cognitive model of sequential decision making is proposed, which learns preferences over choice attributes, allowing for the generalisation of preferences to unseen options, by virtue of their similarity to previous choices. This model explains reduced exploration patterns behaviour observed in the supermarket and preferential choices in more controlled laboratory settings. Overall, this suggests that consumers depend on associative systems in long-term memory when navigating large spaces of options, enabling inferences about the conceptual properties and subjective value of novel options

Sifting customers from the clickstream : behavior pattern discovery in a virtual shopping environment

While shopping online, customers\u27 needs and goals may change dynamically, based on a variety of factors such as product information and characteristics, time pressure and perceived risk. While these changes create emergent information needs, decisions about what information to present to customers are typically made before customers have visited a web site, using data such as purchase histories and logs of web pages visited. Better understanding of customer cognition and behavior as a function of various factors is needed in order to enable the right information to be presented at the right time. One approach to achieving this understanding is to develop predictions about what information to present based on inferences made from cognitively-grounded models of the customer, calibrated according to an analysis of what behaviors can be observed during the online shopping experience (e.g., clickstream produced by mouse clicks and typing). As a step in achieving this objective, this research tests hypotheses about how differences in product involvement, time pressure, and uncertainty and riskiness of choice may impact a customer\u27s search and decision strategies, time on task, and perceived risk while shopping online. It draws upon the results of prior research, as well as two pilot studies, to motivate the design of a study involving human participants making purchasing decisions in an online shopping environment. The main data sources are the think-aloud protocols and clickstreams of the participants, as well as pre- and post-experiment questionnaires. This work is expected to improve understanding of how contextual, personal and product-related factors help shape online shopping behavior, and to generate insights into the cognitive processes that inform this behavior. Future work beyond the thesis is likely to involve more formal modeling of human cognition in online shopping environments

Discovering user intent In E-commerce clickstreams

E-commerce has revolutionised how we browse and purchase products and services globally. However, with revolution comes disruption as retailers and users struggle to keep up with the pace of change. Retailers are increasingly using a varied number of machine learning techniques in areas such as information retrieval, user interface design, product catalogue curation and sentiment analysis, all of which must operate at scale and in near real-time. Understanding user purchase intent is important for a number of reasons. Buyers typically represent <5% of all e-commerce users, but contribute virtually all of the retailer profit. Merchants can cost-effectively target measures such as discounting, special offers or enhanced advertising at a buyer cohort - something that would be cost prohibitive if applied to all users. We used supervised classic machine learning and deep learning models to infer user purchase intent from their clickstreams. Our contribution is three-fold: first we conducted a detailed analysis of explicit features showing that four broad feature classes enable a classic model to infer user intent. Second, we constructed a deep learning model which recovers over 98% of the predictive power of a state-of-the-art approach. Last, we show that a standard word language deep model is not optimal for e-commerce clickstream analysis and propose a combined sampling and hidden state management strategy to improve the performance of deep models in the e-commerce domain. We also propose future work in order to build on the results obtained

Distributed Load Testing by Modeling and Simulating User Behavior

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system or its usage. Lack of adaptation can prevent the integration of load testing into system quality assurance, leading to an incomplete evaluation of system quality. The goal of this research is to improve the state of software engineering by addressing open challenges in load testing of human-machine systems with a novel process that a) models and classifies user behavior from streaming and aggregated log data, b) adapts to changes in system and user behavior, and c) generates distributed workload by realistically simulating user behavior. This research contributes a Learning, Online, Distributed Engine for Simulation and Testing based on the Operational Norms of Entities within a system (LODESTONE): a novel process to distributed load testing by modeling and simulating user behavior. We specify LODESTONE within the context of a human-machine system to illustrate distributed adaptation and execution in load testing processes. LODESTONE uses log data to generate and update user behavior models, cluster them into similar behavior profiles, and instantiate distributed workload on software systems. We analyze user behavioral data having differing characteristics to replicate human-machine interactions in a modern microservice environment. We discuss tools, algorithms, software design, and implementation in two different computational environments: client-server and cloud-based microservices. We illustrate the advantages of LODESTONE through a qualitative comparison of key feature parameters and experimentation based on shared data and models. LODESTONE continuously adapts to changes in the system to be tested which allows for the integration of load testing into the quality assurance process for cloud-based microservices