Big data innovation mechanisms in the Canadian television industry

Abstract

As a response to growing fragmentation, niche programming, and increasing competition from other forms of media, the Canadian television industry is undergoing rapid changes through digital innovation. Existing audience information systems are being enhanced with big data in order to allow the television audience marketplace to gain thicker and richer insights about television audiences. This evolution has allowed broadcasters to manufacture new forms of the television audience product with many features that were previously absent in the traditional television audience product. This study investigates the various generative mechanisms allowing Canadian television broadcasters to successfully innovate by enhancing their systems with these new sources of data. Supported by critical realism, affordance theory, and a stepwise analysis method, semi-structured interviews were conducted across three Canadian television broadcasters as a means to understand this digital innovation process. Five generative mechanisms are identified through the digital innovation process allowing for a better understanding of digital innovation with big data, particularly related to the audience product. These findings build on previous work by expanding the current understanding of the digital innovation process as well as the digital infrastructure mechanism by highlighting the central role in which digital infrastructures can play within an industry. Concurrently a new understanding of audiences is further developed by providing evidence that the audience is a manifestation of the underlying data itself – a significant reconceptualization of the audience product. These findings, as well as the analysis process, provide an empirical example of the role of affordances with big data. Despite many calls for research, the role of big data in digital innovation is a significantly underdeveloped area of research. Lastly, by leveraging a quantitative method for pattern identification and grouping, the study further develops the existing critical realist methodology by building upon the analysis process for the identification of generative mechanisms.