Search (2 results, page 1 of 1)

0.024815073 = product of:
  0.09926029 = sum of:
    0.09926029 = weight(_text_:evolution in 5980) [ClassicSimilarity], result of:
      0.09926029 = score(doc=5980,freq=6.0), product of:
        0.19585751 = queryWeight, product of:
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.03697776 = queryNorm
        0.5067985 = fieldWeight in 5980, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5980)
  0.25 = coord(1/4)

Abstract

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.

0.017192384 = product of:
  0.06876954 = sum of:
    0.06876954 = weight(_text_:evolution in 4116) [ClassicSimilarity], result of:
      0.06876954 = score(doc=4116,freq=2.0), product of:
        0.19585751 = queryWeight, product of:
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.03697776 = queryNorm
        0.35112026 = fieldWeight in 4116, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.046875 = fieldNorm(doc=4116)
  0.25 = coord(1/4)

Abstract

Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.