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Browsing

Browsing is an exploratory information-seeking behavior characterized by the iterative process of glimpsing a field of potential objects, selecting and examining specific items, and deciding to acquire (physically or conceptually) or abandon them, often resulting in serendipitous discoveries without a predefined query.^[1] This activity embodies a series of visual or physical scans, driven by curiosity and adaptive navigation, distinguishing it from structured searching by its flexibility and user-directed evolution.^[2] In the field of information science, browsing serves as a complementary strategy to formal querying, enabling users to navigate unstructured or semi-structured collections where needs may shift dynamically, as modeled in the "berrypicking" approach to online searching.^[3] It has long been integral to human information behavior, with roots in physical settings such as library stacks or retail displays, where individuals ignore formal organization to scan and sample materials intuitively.^[4] Psychological studies link this to innate exploratory tendencies, akin to animal foraging, emphasizing its role in orientation, comparison, and identification within diverse media like text, images, or multimedia.^[1]^[5] The digital era has amplified browsing's prominence through web-based systems, where hyperlinks, thumbnails, and faceted interfaces support non-linear traversal of vast online repositories, blending it seamlessly with retrieval processes.^[6] In web information retrieval, browsing facilitates both directed tasks—such as scanning lists for known targets—and undirected exploration, enhancing discovery in environments like search engine results or digital libraries.^[7] Key variants include directed browsing, which is systematic and goal-focused; semidirected or predictive browsing, blending partial structure with anticipation; and undirected browsing, fully open-ended and serendipity-oriented.^[1] These modes underscore browsing's adaptability, though challenges like information overload persist in modern digital contexts.^[8]

Definition and Fundamentals

Core Definition

Browsing is defined as a non-linear, exploratory process of scanning and selecting information without a predefined query, often leading to serendipitous discoveries.^[9] This information-seeking behavior involves successive acts of glimpsing potential items of interest, examining them based on visual or contextual cues, and deciding whether to pursue or abandon them, thereby allowing users to navigate collections iteratively rather than linearly.^[10] In contrast to directed search, which relies on specific queries to retrieve targeted results, browsing emphasizes open-ended exploration to uncover unexpected but relevant information.^[4] Key characteristics of browsing include low specificity in initial goals, heavy reliance on visual cues such as layout, titles, or thumbnails for orientation, and iterative navigation through physical or digital collections via mechanisms like hyperlinks or adjacent items.^[9] These traits enable users to sample a broad field of information, adjusting their path dynamically based on emerging interests, which supports both directed and undirected variants depending on the context.^[2] Representative examples include flipping through pages of books on library shelves to identify relevant sections through serendipitous encounters or scrolling through social media feeds and selecting posts based on thumbnails and previews without entering search terms.^[4] The concept of browsing evolved from practices in library science, where it facilitated discovery in physical stacks, to a core strategy in information retrieval systems during the 20th century, adapting to digital interfaces that support user-driven navigation.^[10]

Historical Context

The roots of browsing as a concept in information-seeking trace back to 19th-century library practices, where the transition from closed-stack systems to open stacks enabled users to physically navigate and explore collections. In closed stacks, prevalent until the mid-19th century, patrons requested materials through librarians, limiting direct access; open stacks began to be implemented in academic institutions such as Amherst College in the 1870s under Melvil Dewey's influence and became more widespread in public libraries in the late 19th and early 20th centuries, as exemplified by expansions at the Boston Public Library around 1895.^[11] The formal study of browsing emerged in the 1940s and 1950s alongside the development of information science, as scholars began examining user behaviors in library environments. Jesse Shera, a pioneering figure in the field, contributed to early analyses of how users interacted with information systems in his work on social epistemology and library services during this period.^[12]^[13] Shera's efforts, including collaborations on documentation and retrieval principles, highlighted user-centered access beyond rigid catalog searches.^[14] The 1980s marked a digital shift for browsing, driven by the advent of hypertext systems that enabled non-linear navigation of information. Ted Nelson's Xanadu project, conceived in 1960 and detailed in his 1965 publications, envisioned a global hypertext repository with bidirectional links, profoundly influencing concepts of exploratory browsing by allowing users to traverse interconnected documents freely, distinct from sequential reading.^[15] This laid groundwork for prototypes like Douglas Engelbart's NLS system in 1968, which implemented practical hypertext interfaces for browsing.^[16] Post-2000, browsing integrated deeply into web usability studies, building on 1990s frameworks like Peter Pirolli's information foraging theory, which modeled user navigation as adaptive foraging for valuable information patches online. Pirolli and Stuart Card's seminal 1995 and 1999 papers applied ecological principles to predict browsing patterns, such as scent-following via links, influencing design guidelines for minimizing user effort in digital environments.^[17] These developments emphasized serendipity as a byproduct of unstructured exploration in vast information spaces.^[18]

Strategies and Methods

Orienting Strategies

Orienting in browsing refers to the initial process by which users scan information environments to establish situational awareness, such as skimming physical shelves in libraries or category menus in digital interfaces, allowing them to gauge the scope and structure of available resources.^[10] This phase enables users to form a preliminary mental map of the space, identifying potential entry points without committing to a specific path.^[10] In digital settings, users often follow scanning patterns such as F-shaped or Z-shaped paths when rapidly reviewing web content.^[19] A foundational model for these tactics is Bates' berrypicking framework (1989), which portrays orientation as part of an evolving search where users gather information incrementally, shifting focus based on emerging cues and partial findings to build awareness over time.^[10] In this model, initial scanning evolves through successive refinements, mimicking the act of picking berries along a changing path rather than a single, linear query.^[10] Factors influencing effective orientation include user expertise level, with domain experts relying more on direct navigation to specialized landmarks (e.g., targeted sites like PubMed for medical topics) and issuing precise, vocabulary-rich queries to accelerate awareness-building, compared to novices who engage in broader, less efficient scans.^[20]

Goal-Directed vs. Serendipitous Approaches

Goal-directed browsing represents a structured approach to information seeking where users pursue loosely defined objectives, such as gathering general insights on a historical topic, through systematic sequential scanning of resources and iterative decision points to evaluate and select relevant paths.^[21] This method aligns with analytical strategies that prioritize efficiency in retrieving known or anticipated information, often involving targeted navigation within digital environments like hyperlinked websites or databases.^[22] In contrast, serendipitous browsing emerges from unstructured, unplanned interactions with information, where unexpected discoveries arise from chance encounters, frequently prompted by visual adjacencies, thematic links, or incidental exposures in the browsing environment.^[23] This approach fosters exploratory wandering without predefined targets, allowing users to stumble upon novel content that may connect to latent interests or needs.^[21] The primary differences between these approaches lie in their emphases: goal-directed browsing focuses on recall and operational efficiency to fulfill specific, albeit broad, aims, whereas serendipitous browsing values novelty and the potential for unanticipated relevance, often enhancing discovery in open-ended contexts. Empirical studies of interdisciplinary researchers reveal that serendipitous encounters during information seeking contribute to greater satisfaction, particularly in creative tasks, by providing breakthroughs that enrich the research process beyond planned outcomes.^[24]

Comparisons with Other Information-Seeking Behaviors

Browsing versus Directed Search

Directed search represents a linear, goal-oriented process in information retrieval, where users formulate precise queries—such as keywords or Boolean operators—to target specific results from databases or search engines.^[9] This approach is exemplified in keyword searches within library catalogs or online databases, aiming to efficiently locate known items or verify facts without extraneous exploration.^[9] In contrast, browsing emphasizes an exploratory, non-linear navigation through information spaces, prioritizing breadth over specificity to facilitate incidental discoveries and serendipitous learning.^[9] While directed search seeks depth and exact matches to fulfill well-defined information needs, browsing accommodates broader scanning of resources, such as flipping through journal issues or navigating hyperlinks, often leading to unexpected insights.^[25] These differences highlight browsing's suitability for open-ended exploration versus directed search's focus on precision and efficiency.^[9] A key distinction emerges in relevance and exploration: directed search typically prioritizes targeted results with high relevance to the query, whereas browsing surfaces a wider array of potentially useful information, though with varying relevance, supporting discovery at the risk of overload.^[25] Browsing particularly suits ill-defined problems, where users' needs are vague or evolving, allowing for the emergence of unanticipated requirements through semi-structured scanning rather than rigid querying.^[9] User scenarios further illustrate these contrasts; for instance, directed search is ideal for fact-finding tasks, such as querying "What is the capital of France?" to retrieve a definitive answer from a database.^[9] Conversely, browsing suits inspirational or exploratory pursuits, like wandering through digital galleries to discover various art styles without a predetermined target.^[9] This trade-off underscores browsing's role in fostering creativity amid uncertainty, while directed search streamlines resolution of concrete queries.^[25]

Browsing versus Analytical Strategies

Analytical strategies in information seeking involve a systematic, reductive approach to problem-solving, where users break down complex queries into discrete components, such as key concepts or terms, and evaluate sources methodically to achieve precise outcomes.^[26] This often includes techniques like Boolean logic to refine searches, for example, combining terms with operators such as AND, OR, or NOT to exclude irrelevant results and ensure comprehensiveness.^[27] In contrast, browsing adopts a more holistic and intuitive method, relying on opportunistic scanning and navigation through information spaces without predefined structures, allowing users to recognize relevance through contextual cues rather than explicit criteria.^[28] While analytical strategies emphasize evidence-based verification and logical decomposition, browsing prioritizes fluid exploration, often leading to unexpected connections that analytical methods might overlook.^[26] The theoretical foundation for these differences draws from bounded rationality, as articulated by March and Simon, which posits that decision-makers operate under constraints of incomplete information, limited cognitive capacity, and time, making exhaustive analysis impractical.^[29] In this framework, browsing serves as a satisficing mechanism—accepting "good enough" insights— to compensate for these limitations by enabling users to sample and explore environments efficiently when full rationality is unattainable.^[30] Analytical strategies, conversely, align more closely with aspirational full rationality, attempting to approximate optimal solutions through deliberate planning despite the same bounds.^[31] Outcomes of these approaches diverge notably: analytical strategies typically produce verifiable, targeted results suitable for fact-finding or hypothesis testing, offering high precision but potentially missing broader contexts.^[32] Browsing, however, fosters innovation by facilitating serendipitous discoveries and pattern recognition across disparate sources, supporting creative synthesis in exploratory tasks.^[33] Goal-directed browsing can emerge as a hybrid, blending intuitive navigation with targeted intent to balance these strengths.^[34]

Cognitive and Psychological Dimensions

Mental Models in Browsing

Mental models in browsing represent users' internalized cognitive frameworks of information spaces, allowing them to predict and navigate content organization, such as anticipating that related topics will cluster in hierarchical or thematic structures.^[35] These models function as simplified simulations of how systems operate, drawing from users' experiences to guide exploratory behaviors without exhaustive analysis.^[36] In digital contexts like the Web, users often conceptualize the space as an interconnected network or "superhighway," where information flows predictably based on perceived links and categories.^[35] The development of these mental models during browsing is shaped by prior knowledge and experiential factors, with formation occurring through iterative interactions such as trial-and-error exploration.^[35] Novice users, lacking deep familiarity, tend to rely on superficial cues like interface layouts, icons, and immediate visual feedback to build initial representations, often resulting in fragmented or utilitarian views of the system.^[37] In contrast, more experienced individuals leverage established schemas—preorganized knowledge structures—that enable abstract understanding of underlying information architectures, facilitating quicker adaptation to new environments.^[35] For undergraduate students engaging with Web-based retrieval, this progression is evident in how personal observations and informal learning from peers refine models over repeated sessions.^[35] Donald Norman's 1983 theory of mental models provides a foundational lens for applying these concepts to browsing navigation, emphasizing how users construct explanatory representations to bridge intentions and actions in interactive systems.^[36] In digital interfaces, mismatches arise when the system's presented image—its visible structure and feedback—diverges from users' expectations, leading to navigational errors, increased frustration, and inefficient exploration.^[36] Empirical studies confirm this in web contexts, where users share robust expectations for object placements (e.g., search fields in upper regions), but inconsistencies across site types like news portals or e-commerce pages can disrupt these models and hinder seamless movement.^[38] Well-developed mental models profoundly influence browsing persistence and efficiency by minimizing cognitive load, as aligned expectations allow users to allocate mental resources toward content discovery rather than deciphering the interface.^[39] Users with immature or mismatched models exhibit reduced endurance in tasks, often abandoning searches when faced with unexpected structures, whereas accurate models promote sustained engagement across diverse web interactions.^[39] This effect underscores the role of mental models in supporting orienting strategies, where cognitive ease enables deeper immersion in information spaces.^[39] Recent studies as of 2024 indicate that web-browsing patterns not only reflect users' mood and mental health but also shape them, creating feedback loops that may perpetuate psychological states. Furthermore, interactions with large language models (LLMs) are prompting shifts in mental models, as users adapt to AI-assisted navigation in unconstrained digital environments.^[40]^[41]

Role of Randomness and Serendipity

Randomness is integral to the browsing process, introducing non-deterministic elements that enable users to access information unpredictably and beyond targeted queries. In physical library settings, the linear shelving of materials creates opportunities for random access, where users may encounter adjacent items that diverge from their initial intent, such as flipping through nearby books or journals that reveal unanticipated connections.^[42] Similarly, in digital systems, algorithmic randomization—such as shuffling content in news feeds or recommendation algorithms—simulates this variability, exposing users to diverse items not strictly aligned with their past behavior.^[43] Serendipity emerges from these random encounters as the fortuitous discovery of pertinent information that was not actively sought, often transforming browsing into a source of unexpected value. Coined by Horace Walpole in 1754 and later formalized, serendipity involves not mere chance but the sagacity to recognize and exploit accidental findings; Roberts (1989) specifically characterizes it as "accidental sagacity," underscoring the role of user perceptiveness in turning randomness into insight.^[44] This phenomenon distinguishes browsing from more linear search methods by emphasizing unintended yet beneficial outcomes. Key mechanisms facilitating serendipity in browsing include proximity effects, where the spatial or algorithmic adjacency of resources prompts exploratory deviations, such as a researcher noticing a related title on a nearby shelf or in a suggested sidebar.^[45] User openness to such deviations further amplifies these moments, as individuals with flexible mental states are more likely to pursue tangential leads during browsing sessions.^[45] McBirnie (2008) highlights how this interplay of chance and preparedness underpins serendipitous information behavior in libraries and digital archives.^[46] The benefits of randomness and serendipity in browsing extend to cognitive enrichment, particularly by enhancing creativity through novel associations that challenge conventional thinking. For instance, unexpected discoveries during library shelf browsing have been shown to stimulate innovative ideas by bridging disparate concepts, as evidenced in studies of academic researchers who reported broader interdisciplinary insights from such encounters.^[42] Additionally, these elements promote holistic understanding, allowing users to build comprehensive knowledge frameworks rather than isolated facts, with empirical observations indicating that serendipitous exposures in digital feeds foster reflective connections across topics over time.^[47] Recent research as of 2024 shows that interface enhancements in digital platforms can increase serendipity, positively affecting user trust and exploratory behaviors, though algorithmic personalization may limit such opportunities.^[48]

Controversies and Debates

Debates on Randomness

The core debate in the study of browsing centers on whether the process is fundamentally random or exhibits discernible patterns. Early scholarship, as synthesized by Dervin (1983), portrayed information seeking—including browsing—as characterized by an "essential randomness," where users sample sources in a manner akin to random exploration without predetermined structure.^[49] This perspective drew from observations in library and archival contexts, emphasizing unpredictability in how individuals navigate collections to encounter potentially useful material. Critics of this view, notably Ellis (1989), contended that browsing involves underlying patterns in user paths, framing it as a "semi-directed" activity rather than pure chance.^[50] Ellis's empirical analysis of social scientists' behaviors identified browsing as one of several interconnected strategies, such as chaining references or monitoring sources, which collectively form coherent, goal-oriented trajectories despite apparent flexibility. Arguments supporting randomness highlight statistical models of user interactions in traditional environments like library catalogs, where selections often approximate uniform distributions across available items, suggesting minimal bias in initial choices.^[51] In contrast, evidence against pure randomness emerges from behavioral logs and observational studies, which reveal "pseudo-randomness" influenced by latent goals, prior knowledge, and contextual cues, leading users to favor certain paths even in exploratory modes.^[9] These debates carry significant implications for system design in information environments. Incorporating random elements, such as randomized recommendations, can promote content diversity and serendipitous discoveries, while emphasizing predictable patterns enhances usability and efficient navigation.^[52] Serendipity, often posited as a key outcome of browsing, remains contested in this framework, linking back to broader discussions of randomness's role in unintended beneficial encounters.

Effectiveness in Modern Contexts

In contemporary digital environments, browsing remains effective for tasks involving idea generation and exploratory learning, where serendipitous encounters can yield higher success rates compared to more directed strategies. An empirical study by Foster and Ford (2003) analyzed information-seeking behaviors among academic researchers and found that browsing often facilitated unexpected discoveries contributing to innovative insights, such as new research directions.^[53] This effectiveness stems from browsing's ability to expose users to diverse, unanticipated content, fostering associative thinking that supports problem-solving in open-ended domains like research and design. However, information overload in expansive digital spaces poses significant challenges to browsing's efficacy, often diminishing opportunities for meaningful serendipity by overwhelming users with irrelevant or repetitive content. In platforms like social media and search engines, algorithmic curation can create echo chambers that prioritize familiar information, reducing the randomness essential for serendipitous finds; information overload in digital spaces can lead to reduced serendipity by exposing users to more redundant content compared to pre-digital environments. To mitigate this, curated feeds and personalized recommendation systems have emerged as practical solutions, filtering vast data streams to balance exposure to novel yet relevant material— for instance, tools like RSS aggregators or platform-specific "For You" pages can increase serendipitous engagement while curbing overload perceptions.^[54]^[55] Modern web analytics further underscore browsing's prevalence and utility, with a notable portion of online sessions involving undirected exploration rather than specific queries, particularly among younger users seeking inspiration or leisure. Pew Research Center's 2021 report on internet behaviors indicated significant casual web use for news and entertainment among U.S. adults.^[56] Looking ahead, AI-enhanced browsing tools promise to optimize this approach by algorithmically blending randomness with relevance, such as through hybrid recommendation engines that introduce controlled serendipity—early implementations in AI browsers have shown improvements in user-reported discoveries.^[57]^[58]

Applications and Implications

In Traditional Information Environments

In traditional libraries and archives, browsing has been enabled through physical layouts that promote direct interaction with materials. Open shelf arrangements, a development from the late 19th century onward, allowed patrons to access and scan books directly, fostering serendipitous discoveries by placing related volumes side by side. Card catalogs complemented this by providing alphabetical indexes by author, title, and subject, directing users to shelf locations for further exploration without requiring staff mediation. Periodical rooms offered specialized spaces for casual browsing of current newspapers, magazines, and journals, often organized by publication date or category to support quick, thematic scanning. Central to these environments were design principles that structured collections to enhance adjacency-based discovery. The Dewey Decimal Classification system, introduced by Melvil Dewey in 1876, organizes materials hierarchically by subject using decimal notation, ensuring that works on similar topics are shelved in close proximity. This facilitates visual scanning and unexpected juxtapositions, allowing users to encounter relevant resources through physical contiguity rather than exhaustive searching. Such arrangements prioritized user autonomy in physical spaces, contrasting with closed-stack systems where access was restricted. User studies underscore browsing's prominence in humanities sections, where interdisciplinary and interpretive materials lend themselves to exploratory formats. Humanities scholars frequently report relying on shelf browsing to identify primary sources and forge connections across texts, with surveys indicating that over 70% value physical stacks for this purpose.^[59] For example, Catherine Sheldrick Ross's 2001 study of leisure readers found that many discovered informational value through incidental browsing of library shelves, particularly in literature and history areas, leading to higher engagement and repeated visits compared to more structured disciplines like sciences. However, physical browsing encounters significant challenges from space limitations and digitization trends. Growing collections strain library footprints, prompting many institutions to relocate materials to remote storage and reduce open-shelf access. Digitization efforts, while expanding reach, have curtailed tactile exploration by converting rare items to digital formats, thereby diminishing the serendipity inherent in traditional setups.

In Digital and Web-Based Systems

In digital environments, hyperlinks serve as a foundational mechanism for serendipitous browsing by enabling users to navigate non-linearly across interconnected content, fostering unexpected discoveries akin to physical browsing. Originating from concepts like Ted Nelson's hypertext vision in 1965, hyperlinks in the World Wide Web allow users to follow associative paths, turning structured information into a dynamic exploration tool that promotes chance encounters with relevant material.^[60] This structure supports serendipity by broadening access beyond targeted searches, as users can "stumble" upon linked resources that align with evolving interests.^[60] Infinite scrolling further enhances digital browsing by providing seamless, continuous content loading without pagination breaks, encouraging prolonged engagement and exploratory behavior on platforms like news aggregators and e-commerce sites. Introduced prominently around 2006, this technique reduces navigational friction, making it ideal for mobile interfaces where users scroll through expansive feeds, such as on TikTok or Instagram, to mimic the fluidity of flipping through a magazine.^[61] However, it can complicate refinding specific items, potentially undermining precise recall in favor of immersive discovery.^[61] Recommendation algorithms in digital systems increasingly incorporate serendipity by balancing relevance with novelty, suggesting unexpected yet valuable items to expand user horizons beyond familiar patterns. Formalized in recommender systems research, serendipity is measured through metrics combining unexpectedness and utility, as explored in experimental designs that evaluate user satisfaction from such suggestions.^[62] For instance, algorithms may introduce controlled diversity in outputs, drawing from user history to recommend items that are novel but aligned, thereby simulating serendipitous encounters in vast digital corpora. Recent advancements as of 2025 include AI models like large language models enhancing personalized discovery in tools such as generative search interfaces.^[62] Key platforms exemplify these implementations. Google Discover delivers personalized content feeds based on user activity, surfacing timely articles and topics without explicit queries to promote passive browsing and serendipitous insights into interests like health or sports.^[63] In library online public access catalogs (OPACs), faceted browsing allows users to refine searches by attributes such as format, language, or subject, enabling iterative exploration of collections; surveys show nearly all academic OPACs (100%) include format facets, with an average of 9.8 facets per interface to support structured yet flexible discovery.^[64] Social media timelines function as algorithmic browsing interfaces, presenting chronological or curated feeds that facilitate serendipity through exposure to diverse posts, as seen in Twitter where unfocused scrolling during breaks leads to unexpected academic or professional encounters.^[65] Tools like StumbleUpon, launched in 2001, pioneered controlled randomness by using a "Stumble" button and user ratings to generate semi-random website recommendations tailored to interests, peaking at over a billion monthly interactions in the early 2010s and influencing modern discovery apps.^[66] This model evolved into successors like Mix, emphasizing user-curated serendipity over pure chance.^[66] Despite these advances, algorithmic biases in digital browsing often limit true serendipity by creating echo chambers, where personalized feeds reinforce existing views and reduce exposure to diverse content. Eli Pariser's 2011 concept of the "filter bubble" highlights how search engines and social platforms tailor results—such as varying "BP" queries yielding investment tips for one user and oil spill news for another—due to opaque personalization, potentially homogenizing information diets.^[67] Studies confirm these biases amplify ideological isolation, underscoring the need for transparency in algorithmic design to restore broader discovery.^[67]

References

[1]
What is browsing—really? A model drawing from behavioural ...
Sep 15, 2007 · Browsing embodies an act of scanning, which has been variously described as looking, examining, or sampling where the person's body or eyes move ...
[2]
INFORMATION RETRIEVAL BY BROWSING
THE BROWSING MODEL COMPARED TO THE QUERY MODEL. Browsing can be defined as an interactive search activity in which the direction of the search is determined ...
[3]
The Design of Browsing and Berrypicking Techniques
However, as Ellis notes [16], browsing is an important part of standard information searching; he calls it "semi-directed or semi-structured searching" when ...
[4]
Browsing, Searching, and Linking in Relation
Browsing, in its nature, ignores the file structure or other formal organization of information. For example, when we browse in library stacks or in a bookstore ...
[5]
A Descriptive Study of the Functional Components of Browsing.
The paper describes a descriptive study of the functional components of browsing, which is viewed as the strategic and adaptive technique that people use to ...
[6]
2. Basic Concepts
Both retrieval and browsing are, in the language of the World Wide Web, `pulling' actions. That is, the user requests the information in an interactive manner.
[7]
View of Information seeking on the Web: An integrated model of ...
Directed browsing occurs when browsing is systematic, focused, and directed by a specific object or target. Examples include scanning a list for a known item, ...
[8]
[PDF] Browsing within an academic library - BYU ScholarsArchive
Browsing in academic libraries is used for exploratory research, often directed within a discipline, and can be serendipitous, but in-person browsing has ...
[9]
What is browsing—really? A model drawing from behavioural ...
Sep 15, 2007 · Browsing is the activity of engaging in a series of glimpses, each of which may or may not lead to closer examination of a (physical or ...
[10]
[PDF] The design of browsing and berrypicking techniques for the online ...
First, a new model of searching in online and other information systems, called “berrypicking,” is discussed. This model, it is argued, is much closer.
[11]
[PDF] Serendipity in the Stacks: Libraries, Information Architecture, and the ...
Chan, library and information science researchers have generally characterized browsing in the stacks as falling into three basic categories: (1) “search ...
[12]
The Permanent Library of the Now | KNOW
The industrial book stacks of the late nineteenth century, still ubiquitous in libraries today, are remarkably robust and flexible systems that can ...
[13]
Information science - The Knowledge Book
... Jesse Shera, in the 1960s. According to Shera ... browsing that would enable library users to ... Information. Type: Chapter. Information. The Knowledge Book. Key ...
[14]
[PDF] 0 on browsing: the use of search theory in the search for information
Browsing may be defined as a search, hopefully seren- dipitous. In connection with a library, one may browse through the display of recent books to see what ...
[15]
(PDF) Theoretical development of information science: A brief history
Aug 7, 2025 · Library scientist Jesse Shera (1903–1982) and his colleague Margaret ... browsing easily produces many other theories. Thus, many ...
[16]
Ted Nelson Coins the Terms Hypertext, Hypermedia, and Hyperlink
as chief programmer) was the first to implement the hyperlink concept for scrolling within a single document (1966), and soon after for connecting between ...
[17]
Ted Nelson - Who invented Hypertext, Web History | LivingInternet
Ted Nelson invented hypertext, the concept behind links on the web, influencing several developers of the Internet, most notably Tim Berners-Lee.
[18]
Information foraging in information access environments
Information foraging in information access environments. Authors: Peter Pirolli ... Journal of the American Society for Information Science, 40, 68-85.
[19]
Information Foraging: A Theory of How People Navigate on the Web
Nov 10, 2019 · Information foraging was developed at PARC (former XEROX PARC) by Peter Pirolli and Stuart Card in the late 1990s and was inspired by animal ...
[20]
[PDF] Characterizing the Influence of Domain Expertise on Web Search ...
In this paper we present a large-scale, longitudinal, log-based analysis of the effect of domain expertise on web search behavior in four different domains ( ...
[21]
Understanding Differences in Wayfinding Strategies - Hegarty - 2023
Jan 1, 2022 · This analysis reveals factors that affect navigation strategy, including availability of the relevant type of environmental knowledge, momentary ...
[22]
https://www.cambridge.org/core/books/information-seeking-in-electronic-environments/475F4CD0A9F0CB7AA8EFC16C52676A68
[23]
Information Seeking in Electronic Environments
Information Seeking in Electronic Environments. Information Seeking in Electronic Environments ... Marchionini, Gary 1995. User-centered methods for library ...
[24]
The process of serendipity in knowledge work - ACM Digital Library
The process of serendipity in knowledge work. Authors: Lori McCay-Peet. Lori McCay-Peet. iLab, Dalhousie University, Halifax, NS, Canada. View Profile.
[25]
ON USER STUDIES AND INFORMATION NEEDS | Emerald Insight
Jan 1, 1981 · The aim of this paper is to attempt to reduce this confusion by devoting attention to the definition of some concepts and by proposing the basis for a theory.
[26]
Relevance for browsing, relevance for searching - Wiley Online Library
Nov 2, 2005 · In this article, our concern is with how relevance differs in browsing versus directed search. For our purposes, therefore, browsing is defined ...
[27]
Analytical search strategies (Chapter 5) - Cambridge University Press
In this chapter, we describe several analytical strategies to illustrate how electronic environments have changed information seeking by allowing searchers to ...
[28]
[PDF] Analytical Search Strategies: A Tip Sheet with Examples for ...
An analytical search strategy is not browsing. Browsing is opportunistic and requires one to recognize relevant information. So, browsing, in general, is ...Missing: seeking | Show results with:seeking
[29]
[PDF] Chapter 6. Browsing Strategies
In general, browsing is an approach to information seeking that is informal and opportunistic, and depends heavily on the information environment. Four specific.
[30]
Organizations - James G. March, Herbert A. Simon - Google Books
This book offers invaluable insight on all aspects of organizational culture through deep discussion of organization theory.
[31]
[PDF] What is enough? Satisficing information needs - OCLC
This paper examines the theoretical concepts – role theory, rational choice, and satisficing – by attempting to explain the parameters within which users ...Missing: bounded | Show results with:bounded
[32]
Bounded Rationality - Stanford Encyclopedia of Philosophy
Nov 30, 2018 · Bounded rationality now describes a wide range of descriptive, normative, and prescriptive accounts of effective behavior which depart from the assumptions of ...
[33]
EJ376630 - Browse and Analytical Search Strategies in a Full-Text ...
This study examined the effectiveness of a simple browse search strategy and a more complex analytical strategy with 25 high school students, ...
[34]
Encouraging serendipity in research: Designing technologies to ...
This study contributes towards an understanding of how mobile users make connections with new information, furthering our understanding of how serendipitous ...Missing: seminal | Show results with:seminal
[35]
Browsing strategies (Chapter 6) - Information Seeking in Electronic ...
In general, browsing is an approach to information seeking that is informal and opportunistic and depends heavily on the information environment. Four browsing ...Missing: versus | Show results with:versus
[36]
[PDF] Undergraduate students' mental models of the Web as an ...
This study explored undergraduate students' mental models of the Web as an information retrieval system. Mental models play an important role in people's ...
[37]
[PDF] Some Observations on Mental Models - Interactive Design
In the consideration of mental models we need really consider four different things: the target system, the conceputal model of that target system, the user's.
[38]
Examining the classification and evolution of novice users' mental ...
Feb 21, 2019 · The main task of this article is to develop a classification system for novice users' mental models of an academic database and to elaborate ...
[39]
Mental models for web objects: Where do users expect to find the ...
Users generally agree about the locations of many, but not all, web objects. These mental models are robust to demographic factors like gender and web expertise ...
[40]
The influence of mental models and goals on search patterns during ...
This article reports on a study that uses a new analysis and display tool to examine the influences of understanding the system and goals on end-user Internet ...
[41]
[PDF] Building Serendipity into Recommender Algorithms on Online ...
Recommender systems could be redesigned to place serendipity at the heart of the user experience. This includes periodically including more random items in the ...
[42]
Serendipity dimensions and users' information behaviour in the ...
Dec 4, 2008 · This paper outlines an exploratory study concerned with what types of information behaviour users employ in order to find materials in a public library.
[43]
https://lthj.qut.edu.au/article/download/3730/1558/14704
[44]
[PDF] Dervin, Brenda. 1983. Information as a user construct
thrust in the literature is the idea of the essential randomness of information seeking. Brittain (1970) referred indirectly to this point when he suggested ...
[45]
A behavioural model for information retrieval system design
D. Ellis, A behavioural approach to information retrieval system design. Accepted for publication in Journal of Documentation (1989). Web of Science.
[46]
Browsing Behavior in information seeking process: On the basis of ...
Aug 6, 2025 · Matsuda (2004) defined browsing on the basis of behaviours of people in bookstores and libraries: browsing is one of approaches for selecting ...Missing: scholarly | Show results with:scholarly
[47]
The importance of theories of knowledge: Browsing as an example
Jan 25, 2011 · The article argues that human browsing should not be conceptualized primarily in biological terms and should not be understood as random ...Missing: debates | Show results with:debates<|control11|><|separator|>
[48]
[PDF] Blind Spots in AI: the Role of Serendipity and Equity in Algorithm ...
Throughout this work, we demonstrate these principles by means of an hypothetical case example of a job-matching system that aims to help job seekers find.<|control11|><|separator|>
[49]
Why Content Curation Is Essential in Modern Knowledge Management
Aug 31, 2022 · One of the main reasons people prefer curated content is because it reduces their perception of information overload, according to research from ...
[50]
Why Content Curation Matters in the Age of Information Overload
May 30, 2024 · Content curation matters because it eliminates the noise. It makes room for valuable, organized, and thoughtful content around a specific topic.Missing: browsing | Show results with:browsing
[51]
The Internet and the Pandemic | Pew Research Center
Sep 1, 2021 · About two-thirds of Americans (68%) say the interactions they would have had in person, but instead had online or over the phone, have generally ...How the internet and... · The role of technology in... · AcknowledgmentsMissing: behaviors sessions
[52]
AI Browsers & What Marketing Leaders Need to Know Now
Aug 7, 2025 · AI browsers aren't just changing how people find information, they're changing how people discover and interact with brands, and the brands ...Missing: randomness relevance
[53]
How AI-Powered Feed Search Personalizes Content - Arena
Nov 12, 2024 · AI-powered feed search is an advanced technology that uses artificial intelligence to personalize content for readers.Analyzing User Data For... · Algorithmic Bias · You May Also Like
[54]
Editorial: Serendipity and information discovery - Sage Journals
Mar 9, 2022 · Serendipity describes fortunate or beneficial chance events and in information discovery can be used to describe those occasions when we chance upon precisely ...Missing: randomness | Show results with:randomness
[55]
Infinite Scrolling: When to Use It, When to Avoid It - NN/G
Sep 4, 2022 · Infinite scrolling is a listing-page design approach which loads content continuously as the user scrolls down. It eliminates the need for pagination.Usability Issues Caused by... · Compromise: Infinite Scrolling...
[56]
Rethinking Serendipity in Recommender Systems
In this paper, we explore various definitions of serendipity and propose a novel formalization of what it means for recommendations to be serendipitous.Missing: mimicking | Show results with:mimicking
[57]
Get on Discover | Google Search Central | Documentation
Google's Discover feed shows content related to user interests. This documentation will teach you how to get on Discover and monitor traffic.Android · Content policies · Performance report
[58]
Facet‐based library catalogs: A survey of the landscape - Hall - 2011
Jan 11, 2012 · OPACs may present different facets depending on whether they are utilized for search or browse, as well as depending on what search term(s) are ...
[59]
What Supports Serendipity on Twitter? Online Survey on the Role of ...
Nov 16, 2022 · Serendipity experiences are highly desirable in work life, considering both individuals' learning and organizational innovation capacity.
[60]
'There was almost a utopian feeling to it': How StumbleUpon ... - BBC
Dec 11, 2024 · StumbleUpon, a tool that led users to random websites, had a stranglehold on millennials in the 2010s. Its influence echoes through ...
[61]
Filter bubble | Internet Policy Review
Apr 27, 2019 · Introduced by tech entrepreneur and activist Eli Pariser in 2011, the 'filter bubble' is a persistent concept which suggests that search engines ...