Search (3 results, page 1 of 1)

0.037090525 = product of:
  0.11127157 = sum of:
    0.11127157 = weight(_text_:search in 2714) [ClassicSimilarity], result of:
      0.11127157 = score(doc=2714,freq=22.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.6368113 = fieldWeight in 2714, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2714)
  0.33333334 = coord(1/3)

Abstract

To provide a basis for making predictions of the characteristics of search task (ST), based on work task (WT), and to explore the nature of WT and ST, this study examines the relationships between WT and ST (inter-relationships) and the relationships between the different facets of both WT and ST (intra-relationships), respectively. A faceted classification of task was used to conceptualize work task and search task. Twenty-four pairs of work tasks and their associated search tasks were collected, by semistructured interviews, and classified based on the classification. The results indicate that work task shapes different facets or sub-facets of its associated search tasks to different degrees. Several sub-facets of search task, such as Time (Length), Objective task complexity, and Subjective task complexity, are most strongly affected by work task. The results demonstrate that it is necessary to consider difficulty and complexity as different constructs when investigating their influence on information search behavior. The exploration of intra-relationships illustrates the difference of work task and search task in their nature. The findings provide empirical evidence to support the view that work task and search task are multi-faceted variables and their different effects on users' information search behavior should be examined.

0.022366427 = product of:
  0.06709928 = sum of:
    0.06709928 = weight(_text_:search in 1898) [ClassicSimilarity], result of:
      0.06709928 = score(doc=1898,freq=8.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.3840117 = fieldWeight in 1898, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1898)
  0.33333334 = coord(1/3)

Abstract

Purpose - This study aims to investigate the effects of different search and browse features in digital libraries (DLs) on task interactions, and what features would lead to poor user experience. Design/methodology/approach - Three operational DLs: ACM, IEEE CS, and IEEE Xplore are used in this study. These three DLs present different features in their search and browsing designs. Two information-seeking tasks are constructed: one search task and one browsing task. An experiment was conducted in a usability laboratory. Data from 35 participants are collected on a set of measures for user interactions. Findings - The results demonstrate significant differences in many aspects of the user interactions between the three DLs. For both search and browse designs, the features that lead to poor user interactions are identified. Research limitations/implications - User interactions are affected by specific design features in DLs. Some of the design features may lead to poor user performance and should be improved. The study was limited mainly in the variety and the number of tasks used. Originality/value - The study provided empirical evidence to the effects of interaction design features in DLs on user interactions and performance. The results contribute to our knowledge about DL designs in general and about the three operational DLs in particular.

0.019369897 = product of:
  0.058109686 = sum of:
    0.058109686 = weight(_text_:search in 2442) [ClassicSimilarity], result of:
      0.058109686 = score(doc=2442,freq=6.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.33256388 = fieldWeight in 2442, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2442)
  0.33333334 = coord(1/3)

Abstract

The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals' information behavior. Classifying tasks in a domain has been viewed as a departure point of studies on the relationship between tasks and human information behavior. However, previous task classification schemes either classify tasks with respect to the requirements of specific studies or merely classify a certain category of task. Such approaches do not lead to a holistic picture of task since a task involves different aspects. Therefore, the present study aims to develop a faceted classification of task, which can incorporate work tasks and information search tasks into the same classification scheme and characterize tasks in such a way as to help people make predictions of information behavior. For this purpose, previous task classification schemes and their underlying facets are reviewed and discussed. Analysis identifies essential facets and categorizes them into Generic facets of task and Common attributes of task. Generic facets of task include Source of task, Task doer, Time, Action, Product, and Goal. Common attributes of task includes Task characteristics and User's perception of task. Corresponding sub-facets and values are identified as well. In this fashion, a faceted classification of task is established which could be used to describe users' work tasks and information search tasks. This faceted classification provides a framework to further explore the relationships among work tasks, search tasks, and interactive information retrieval and advance adaptive IR systems design.