Information encountering re-encountered: A conceptual re-examination of serendipity in the context of information acquisition

Purpose In order to understand the totality, diversity and richness of human information behavior, increasing research attention has been paid to examining serendipity in the context of information acquisition. However, several issues have arisen as this research subfield has tried to find its feet; we have used different, inconsistent terminology to define this phenomenon (e.g. information encountering, accidental information discovery, incidental information acquisition), the scope of the phenomenon has not been clearly defined and its nature was not fully understood or fleshed-out. Design/methodology/approach In this paper, information encountering (IE) was proposed as the preferred term for serendipity in the context of information acquisition. Findings A reconceptualized definition and scope of IE was presented, a temporal model of IE and a refined model of IE that integrates the IE process with contextual factors and extends previous models of IE to include additional information acquisition activities pre- and postencounter. Originality/value By providing a more precise definition, clearer scope and richer theoretical description of the nature of IE, there was hope to make the phenomenon of serendipity in the context of information acquisition more accessible, encouraging future research consistency and thereby promoting deeper, more unified theoretical development

The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure

Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc

Youth and Digital Media: From Credibility to Information Quality

Building upon a process-and context-oriented information quality framework, this paper seeks to map and explore what we know about the ways in which young users of age 18 and under search for information online, how they evaluate information, and how their related practices of content creation, levels of new literacies, general digital media usage, and social patterns affect these activities. A review of selected literature at the intersection of digital media, youth, and information quality -- primarily works from library and information science, sociology, education, and selected ethnographic studies -- reveals patterns in youth's information-seeking behavior, but also highlights the importance of contextual and demographic factors both for search and evaluation. Looking at the phenomenon from an information-learning and educational perspective, the literature shows that youth develop competencies for personal goals that sometimes do not transfer to school, and are sometimes not appropriate for school. Thus far, educational initiatives to educate youth about search, evaluation, or creation have depended greatly on the local circumstances for their success or failure

How do different devices impact users' web browsing experience?

The digital world presents many interfaces, among which the desktop and mobile device platforms are dominant. Grasping the differential user experience (UX) on these devices is a critical requirement for developing user focused interfaces that can deliver enhanced satisfaction. This study specifically focuses on the user's web browsing experience while using desktop and mobile. The thesis adopts quantitative methodology. This amalgamation presents a comprehensive understanding of the influence of device specific variables, such as loading speed, security concerns and interaction techniques, which are critically analyzed. Moreover, various UX facets including usability, user interface (UI) design, accessibility, content organization, and user satisfaction on both devices were also discussed. Substantial differences are observed in the UX delivered by desktop and mobile devices, dictated by inherent device attributes and user behaviors. Mobile UX is often associated with personal, context sensitive use, while desktop caters more effectively to intensive, extended sessions. A surprising revelation is the existing discrepancy between the increasing popularity of mobile devices and the persistent inability of many websites and applications to provide a satisfactory mobile UX. This issue primarily arises from the ineffective adaptation of desktop-focused designs to the mobile, underscoring the necessity for distinct, device specific strategies in UI development. By furnishing pragmatic strategies for designing efficient, user-friendly and inclusive digital interfaces for both devices; the thesis contributes significantly to the existing body of literature. An emphasis is placed on a device-neutral approach in UX design, taking into consideration the unique capabilities and constraints of each device, thereby enriching the expanding discourse on multiservice user experience. As well as this study contributes to digital marketing and targe­ted advertising perspe­ctives

How do different devices impact users' web browsing experience?

The digital world presents many interfaces, among which the desktop and mobile device platforms are dominant. Grasping the differential user experience (UX) on these devices is a critical requirement for developing user focused interfaces that can deliver enhanced satisfaction. This study specifically focuses on the user's web browsing experience while using desktop and mobile. The thesis adopts quantitative methodology. This amalgamation presents a comprehensive understanding of the influence of device specific variables, such as loading speed, security concerns and interaction techniques, which are critically analyzed. Moreover, various UX facets including usability, user interface (UI) design, accessibility, content organization, and user satisfaction on both devices were also discussed. Substantial differences are observed in the UX delivered by desktop and mobile devices, dictated by inherent device attributes and user behaviors. Mobile UX is often associated with personal, context sensitive use, while desktop caters more effectively to intensive, extended sessions. A surprising revelation is the existing discrepancy between the increasing popularity of mobile devices and the persistent inability of many websites and applications to provide a satisfactory mobile UX. This issue primarily arises from the ineffective adaptation of desktop-focused designs to the mobile, underscoring the necessity for distinct, device specific strategies in UI development. By furnishing pragmatic strategies for designing efficient, user-friendly and inclusive digital interfaces for both devices; the thesis contributes significantly to the existing body of literature. An emphasis is placed on a device-neutral approach in UX design, taking into consideration the unique capabilities and constraints of each device, thereby enriching the expanding discourse on multiservice user experience. As well as this study contributes to digital marketing and targe­ted advertising perspe­ctives

Quantifying Biases in Online Information Exposure

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this paper, we mine a massive dataset of Web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used Web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."Comment: 25 pages, 10 figures, to appear in the Journal of the Association for Information Science and Technology (JASIST

Digital user's decision journey

The landscape of the Internet is continually evolving. This creates huge opportunities for different industries to optimize vital channels online, resulting in various-forms of new Internet services. As a result, digital users are interacting with many digital systems and they are exhibiting dynamic behaviors. Their shopping behaviors are drastically different today than it used to be, with offline and online shopping interacting with each other. They have many channels to access online media but their consumption patterns on different channels are quite different. They do philanthropy online to help others but their heterogeneous motivations and different fundraising campaigns leads to distinct path-to-contribution. Understanding the digital user’s decision making process behind their dynamic behaviors is critical as they interact with various digital systems for the firms to improve user experience and improve their bottom line. In this thesis, I study digital users’ decision journeys and the corresponding digital technology firms’ strategies using inter-disciplinary approaches that combine econometrics, economic structural modeling and machine learning. The uncovered decision journey not only offer empirical managerial insights but also provide guideline for introducing intervention to better serve digital users