Search (17 results, page 1 of 1)

0.017433718 = product of:
  0.034867436 = sum of:
    0.017165681 = weight(_text_:information in 443) [ClassicSimilarity], result of:
      0.017165681 = score(doc=443,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.19395474 = fieldWeight in 443, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=443)
    0.017701752 = product of:
      0.035403505 = sum of:
        0.035403505 = weight(_text_:organization in 443) [ClassicSimilarity], result of:
          0.035403505 = score(doc=443,freq=2.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.19695997 = fieldWeight in 443, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.0390625 = fieldNorm(doc=443)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

There is no information search system can assist young children's known picture book search needs since the information is not organized according to their cognitive abilities and needs. Therefore, this study explored young children's known picture book search strategies and extracted picture book search elements by simulating a search scenario and playing a picture book search game. The study found 29 elements children used to search for known picture books. Then, these elements are classified into three dimensions: The first dimension is the concept category of an element. The second dimension is an element's status in the story. The third dimension indicates where an element appears in a picture book. Additionally, it revealed a young children's general search strategy: Children first use auditory elements that they hear from the adults during reading. After receiving error returns, they add visual elements that they see by themselves in picture books. The findings can not only help to understand young children's known-item search and reformulation strategies during searching but also provide theoretical support for the development of a picture book information organization schema in the search system.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.1, S.45-57

0.0072827823 = product of:
  0.02913113 = sum of:
    0.02913113 = weight(_text_:information in 5618) [ClassicSimilarity], result of:
      0.02913113 = score(doc=5618,freq=16.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3291521 = fieldWeight in 5618, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=5618)
  0.25 = coord(1/4)

Abstract

This study investigates the influence of individual and information characteristics on university students' information channel selection (that is, search engines, social question & answer sites, online health websites, and social networking sites) of online health information (OHI) for three different types of search tasks (factual, exploratory, and personal experience). Quantitative data were collected via an online questionnaire distributed to students on various postgraduate programs at a large UK university. In total, 291 responses were processed for descriptive statistics, Principal Component Analysis, and Poisson regression. Search engines are the most frequently used among the four channels of information discussed in this study. Credibility, ease of use, style, usefulness, and recommendation are the key factors influencing users' judgments of information characteristics (explaining over 62% of the variance). Poisson regression indicated that individuals' channel experience, age, student status, health status, and triangulation (comparing sources) as well as style, credibility, usefulness, and recommendation are substantive predictors for channel selection of OHI.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.2, S.196-207

0.0072827823 = product of:
  0.02913113 = sum of:
    0.02913113 = weight(_text_:information in 442) [ClassicSimilarity], result of:
      0.02913113 = score(doc=442,freq=16.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3291521 = fieldWeight in 442, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=442)
  0.25 = coord(1/4)

Abstract

Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.1, S.3-18

0.006068985 = product of:
  0.02427594 = sum of:
    0.02427594 = weight(_text_:information in 5882) [ClassicSimilarity], result of:
      0.02427594 = score(doc=5882,freq=16.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.27429342 = fieldWeight in 5882, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5882)
  0.25 = coord(1/4)

Abstract

Due to the advances in information retrieval in the past decades, search engines have become extremely efficient at acquiring useful sources in response to a user's query. However, for more prolonged and complex information seeking tasks, these search engines are not as well suited. During complex information seeking tasks, various stages may occur, which imply varying support needs for users. However, the implications of theoretical information seeking models for concrete search user interfaces (SUI) design are unclear, both at the level of the individual features and of the whole interface. Guidelines and design patterns for concrete SUIs, on the other hand, provide recommendations for feature design, but these are separated from their role in the information seeking process. This chapter addresses the question of how to design SUIs with enhanced support for the macro-level process, first by reviewing previous research. Subsequently, we outline a framework for complex task support, which explicitly connects the temporal development of complex tasks with different levels of support by SUI features. This is followed by a discussion of concrete system examples which include elements of the three dimensions of our framework in an exploratory search and sensemaking context. Moreover, we discuss the connection of navigation with the search-oriented framework. In our final discussion and conclusion, we provide recommendations for designing more holistic SUIs which potentially evolve along with a user's information seeking process.

Source

Understanding and improving information search [Vgl. unter: https://www.researchgate.net/publication/341747751_Designing_Multistage_Search_Systems_to_Support_the_Information_Seeking_Process]

0.006068985 = product of:
  0.02427594 = sum of:
    0.02427594 = weight(_text_:information in 528) [ClassicSimilarity], result of:
      0.02427594 = score(doc=528,freq=16.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.27429342 = fieldWeight in 528, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=528)
  0.25 = coord(1/4)

Abstract

Despite being necessary, keeping up to date with new information and trends remains challenging in many fields due to information overload, time constraints, and insufficient evaluation skills. Collaboration, or sharing the effort among group members, may be a solution, but more knowledge is needed. To guide future research on the potential role of collaboration in keeping up to date, we conducted a systematic literature review with a framework synthesis aimed to adapt the conceptual framework for environmental scanning to a collaborative context. Our specific objectives were to identify the factors and outcomes of collaborative information seeking (CIS) and use them to propose an adapted conceptual framework. Fifty-one empirical studies were included and synthesized using a hybrid thematic synthesis. The adapted framework includes seven types of influencing factors and five types of outcomes. Our review contributes to the theoretical expansion of knowledge on CIS in general and provides a conceptual framework to study collaboration in keeping up to date. Overall, our findings will be useful to researchers, practitioners, team leaders, and system designers implementing and evaluating collaborative information projects.

Series

JASIS&Tspecial issue on information behavior and information practices theory

Source

Journal of the Association for Information Science and Technology. 73(2022) no.4, S.542-560

0.005255895 = product of:
  0.02102358 = sum of:
    0.02102358 = weight(_text_:information in 5817) [ClassicSimilarity], result of:
      0.02102358 = score(doc=5817,freq=12.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.23754507 = fieldWeight in 5817, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5817)
  0.25 = coord(1/4)

Abstract

Information seeking and access are essential for users in all walks of life, from addressing personal needs such as finding flights to locating information needed to complete work tasks. Over the past decade or so, the general needs of people with impairments have increasingly been recognized as something to be addressed, an issue embedded both in international treaties and in state legislation. The same tendency can be found in research, where a growing number of user studies including people with impairments have been conducted. The purpose of these studies is typically to uncover potential barriers for access to information, especially in the context of inaccessible search user interfaces. This literature review provides an overview of research on the information seeking and searching of users with impairments. The aim is to provide an overview to both researchers and practitioners who work with any of the user groups identified. Some diagnoses are relatively well represented in the literature (for instance, visual impairment), but there is very little work in other areas (for instance, autism) and in some cases no work at all (for instance, aphasia). Gaps are identified in the research, and suggestions are made regarding areas where further research is needed.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.5, S.596-611

0.005149705 = product of:
  0.02059882 = sum of:
    0.02059882 = weight(_text_:information in 5865) [ClassicSimilarity], result of:
      0.02059882 = score(doc=5865,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.23274569 = fieldWeight in 5865, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=5865)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 71(2020) no.5, S.616

0.0042914203 = product of:
  0.017165681 = sum of:
    0.017165681 = weight(_text_:information in 5613) [ClassicSimilarity], result of:
      0.017165681 = score(doc=5613,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.19395474 = fieldWeight in 5613, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5613)
  0.25 = coord(1/4)

Abstract

Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR-13, suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.2, S.143-157

0.0042914203 = product of:
  0.017165681 = sum of:
    0.017165681 = weight(_text_:information in 1040) [ClassicSimilarity], result of:
      0.017165681 = score(doc=1040,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.19395474 = fieldWeight in 1040, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1040)
  0.25 = coord(1/4)

Abstract

Exploratory searches involve significant cognitively demanding aiming at learning and investigation. However, users gain little support from search engines for their cognitive and metacognitive activities (e.g., discovery, synthesis, planning, transformation, monitoring, and reflection) during exploratory searches. To better support the exploratory search process, we designed a new search assistance tool called OrgBox. OrgBox allows users to drag-and-drop information they find during searches into "boxes" and "items" that can be created, labeled, and rearranged on a canvas. We conducted a controlled, within-subjects user study with 24 participants to evaluate the OrgBox versus a baseline tool called the OrgDoc that supported rich-text features. Our findings show that participants perceived the OrgBox tool to provide more support for grouping and reorganizing information, tracking thought processes, planning and monitoring search and task processes, and gaining a visual overview of the collected information. The usability test reveals users' preferences for simplicity, familiarity, and flexibility of the design of OrgBox, along with technical problems such as delay of response and restrictions of use. Our results have implications for the design of search-assisting systems that encourage cognitive and metacognitive activities during exploratory search processes.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.9, S.1049-1066

0.0037164795 = product of:
  0.014865918 = sum of:
    0.014865918 = weight(_text_:information in 495) [ClassicSimilarity], result of:
      0.014865918 = score(doc=495,freq=6.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16796975 = fieldWeight in 495, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=495)
  0.25 = coord(1/4)

Abstract

In information seeking and retrieval research, researchers often collect data about users' behaviors to predict task characteristics and personalize information for users. The reliability of user behavior may be directly influenced by data collection methods. This article reports on a mixed-methods study examining the impact of study setting (laboratory setting vs. remote setting) and task authenticity (authentic task vs. simulated task) on users' online browsing and searching behaviors. Thirty-six undergraduate participants finished one lab session and one remote session in which they completed one authentic and one simulated task. Using log data collected from 144 task sessions, this study demonstrates that the synthetic lab study setting and simulated tasks had significant influences mostly on behaviors related to content pages (e.g., page dwell time, number of pages visited per task). Meanwhile, first-query behaviors were less affected by study settings or task authenticity than whole-session behaviors, indicating the reliability of using first-query behaviors in task prediction. Qualitative interviews reveal why users were influenced. This study addresses methodological limitations in existing research and provides new insights and implications for researchers who collect online user search behavioral data.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.3, S.362-375

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 327) [ClassicSimilarity], result of:
      0.014565565 = score(doc=327,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 327, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=327)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 72(2021) no.8, S.1011-1027

0.0034331365 = product of:
  0.013732546 = sum of:
    0.013732546 = weight(_text_:information in 693) [ClassicSimilarity], result of:
      0.013732546 = score(doc=693,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.1551638 = fieldWeight in 693, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=693)
  0.25 = coord(1/4)

Source

Information - Wissenschaft und Praxis. 72(2021) H.5/6, S.265-270

0.0030344925 = product of:
  0.01213797 = sum of:
    0.01213797 = weight(_text_:information in 307) [ClassicSimilarity], result of:
      0.01213797 = score(doc=307,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 307, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=307)
  0.25 = coord(1/4)

Abstract

This is the first study that compares types of orientation tactics that blind and sighted users applied in their initial interactions with a digital library (DL) and the associated factors. Multiple methods were employed for data collection: questionnaires, think-aloud protocols, and transaction logs. The paper identifies seven types of orientation tactics applied by the two groups of users. While sighted users focused on skimming DL content, blind users concentrated on exploring DL structure. Moreover, the authors discovered 13 types of system, user, and interaction factors that led to the use of orientation tactics. More system factors than user factors affect blind users' tactics in browsing DL structures. The findings of this study support the social model that the sight-centered design of DLs, rather than blind users' disability, prohibits them from effectively interacting with a DL. Simultaneously, the results reveal the limitation of existing interactive information retrieval models that do not take people with disabilities into consideration. DL design implications are discussed based on the identified factors.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.8, S.995-1010

0.0025748524 = product of:
  0.01029941 = sum of:
    0.01029941 = weight(_text_:information in 635) [ClassicSimilarity], result of:
      0.01029941 = score(doc=635,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.116372846 = fieldWeight in 635, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=635)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1092-1105

0.0025748524 = product of:
  0.01029941 = sum of:
    0.01029941 = weight(_text_:information in 644) [ClassicSimilarity], result of:
      0.01029941 = score(doc=644,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.116372846 = fieldWeight in 644, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=644)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1171-1191

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 5675) [ClassicSimilarity], result of:
      0.008582841 = score(doc=5675,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 5675, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5675)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 71(2020) no.3, S.251-263

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 5945) [ClassicSimilarity], result of:
      0.008582841 = score(doc=5945,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 5945, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5945)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 71(2020) no.8, S.902-915