One of the major terminological forces driving information and communication technology (ICT) integration in research today is “big data.” The characteristics of big data make big data sound inclusive and integrative. However, in practice such approaches are highly selective, excluding input that cannot be effectively structured, represented, or digitized; in other words, excluding complex data. Yet complex data are precisely the kind that human activity tends to produce, but the technological imperative to enhance signal through the reduction of noise does not accommodate this richness. The objective of this chapter is to explore the impact of bias in digital approaches to knowledge creation by investigating the delimiting effect digital mediation and datafication can have on rich, complex cultural data. If rich or complex data prove difficult to fully represent on a small-scale level, in the transition to a big data environment, we run the risk of losing much of what makes this material useful or interesting in the first place. We will begin by reviewing some of the existing implicit definitions of data that underlie ICT-driven research. In doing so will draw attention to the heterogeneity of definitions of data, to identify the key terms associated with data demarcation and data use, and to then expand on the implications of this heterogeneity.
Research on the Impact of Multi-source Intellectual Capital Information Disclosure on Corporate Value in the Big Data Environment-Based on Mediating Effect of Investor 's Confidence
