Search (3 results, page 1 of 1)

  • × author_ss:"Goh, D.H.-L."
  1. Tan, E.M.-Y.; Goh, D.H.-L.: ¬A study of social interaction during mobile information seeking (2015) 0.02
    0.024918701 = product of:
      0.099674806 = sum of:
        0.099674806 = weight(_text_:social in 2215) [ClassicSimilarity], result of:
          0.099674806 = score(doc=2215,freq=12.0), product of:
            0.1847249 = queryWeight, product of:
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.046325076 = queryNorm
            0.5395851 = fieldWeight in 2215, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2215)
      0.25 = coord(1/4)
    
    Abstract
    With the increasing importance of social media in people's lives, more mobile applications have incorporated features to support social networking activities. These applications enable communication between people, using features such as chatting and blogging. There is, however, little consideration of the collaboration between people during information seeking. Mobile applications should support the seeking, sharing, confirming, and validating of information systematically to help users complete their tasks and fulfill their information needs. To support information seeking, especially collaboratively as a group, there is a need to understand people's social interaction behavior. Using tourism as a domain, we conducted a diary study to look into tourists' social interaction during information seeking. Further, based on the diary study findings and current research, we describe a set of triggers that lead to collaboration for each step in the information-seeking process. Here we present the social collaboration patterns between tourists and the people around them. Further, based on a diary study and current research, we describe a set of triggers that lead to collaboration for each step in the BIG6 information-seeking process.
  2. Wu, Q.; Lee, C.S.; Goh, D.H.-L.: Understanding user-generated questions in social Q&A : a goal-framing approach (2023) 0.02
    0.017620182 = product of:
      0.07048073 = sum of:
        0.07048073 = weight(_text_:social in 1021) [ClassicSimilarity], result of:
          0.07048073 = score(doc=1021,freq=6.0), product of:
            0.1847249 = queryWeight, product of:
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.046325076 = queryNorm
            0.3815443 = fieldWeight in 1021, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1021)
      0.25 = coord(1/4)
    
    Abstract
    In social Q&A, user-generated questions can be viewed as goal expressions shaping the responses. Several studies have identified askers' goals from questions. However, it remains unclear how questions set goals for responders. To fill this gap, this research applies goal-framing theory. Goal-frames influence responses by attracting responders' attention to different goals. Eight question cues are used to identify gain, hedonic and normative goal-frames. A total of 14,599 posts are collected. To investigate the influence of goal-frames, response networks are constructed. Results reveal that gain goal-frames attract interactions with questions, while hedonic, and normative goal-frames promote interactions among responses. Further, topic types influence the effects of goal-frames. Gain goal-frames increase interactions with questions in Science, Technology, Engineering, and Mathematics (STEM) topics while hedonic and normative goal-frames attract interactions in non-STEM topics. This research leverages responders' perspectives to explain responses to questions, which are influenced by the goals set up by question cues. Beyond that, our findings enrich the empirical knowledge of social Q&A topics, revealing that the influence of questions varies across STEM and non-STEM topics because the question cues for specifying goals are different in the two topics. Our research opens new directions to investigate questions from responders' perspectives.
  3. Lee, S.-S.; Theng, Y.-L.; Goh, D.H.-L.: Creative information seeking : part II: empirical verification (2007) 0.00
    0.004707306 = product of:
      0.018829225 = sum of:
        0.018829225 = product of:
          0.03765845 = sum of:
            0.03765845 = weight(_text_:22 in 813) [ClassicSimilarity], result of:
              0.03765845 = score(doc=813,freq=2.0), product of:
                0.16222252 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046325076 = queryNorm
                0.23214069 = fieldWeight in 813, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=813)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Date
    23.12.2007 12:22:16