Search (2 results, page 1 of 1)

0.014264907 = product of:
  0.09985434 = sum of:
    0.09985434 = weight(_text_:digital in 1091) [ClassicSimilarity], result of:
      0.09985434 = score(doc=1091,freq=16.0), product of:
        0.16201277 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.04107254 = queryNorm
        0.6163362 = fieldWeight in 1091, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1091)
  0.14285715 = coord(1/7)

Abstract

The number of digital immigrants using new technologies such as smartphones is rapidly increasing. However, digital immigrants still struggle to actually use and benefit from digital technology. This article examines the role of family support in digital immigrants' use of more smartphone functions based on information technology (IT) identity theory. We surveyed 241 digital immigrants who owned smartphones and used structural equation modeling (PLS-SEM) for analysis. We examined the contributing roles of family support for digital immigrants' IT identity and extended use behavior. Family cognitive and emotional support can shape IT identity by improving the smartphone-related experience. Family support has a positive impact on digital immigrants' self-efficacy, embeddedness, perceived usefulness, and perceived enjoyment of using a smartphone. Positive usage experience can also facilitate the establishment of IT identity, which is a key predictor of smartphone use behavior. A strong IT identity also promotes extended use behavior. We discuss the contributions and implications of our findings.

0.005311793 = product of:
  0.03718255 = sum of:
    0.03718255 = weight(_text_:processing in 234) [ClassicSimilarity], result of:
      0.03718255 = score(doc=234,freq=2.0), product of:
        0.1662677 = queryWeight, product of:
          4.048147 = idf(docFreq=2097, maxDocs=44218)
          0.04107254 = queryNorm
        0.22363065 = fieldWeight in 234, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.048147 = idf(docFreq=2097, maxDocs=44218)
          0.0390625 = fieldNorm(doc=234)
  0.14285715 = coord(1/7)

Abstract

The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services.