Development of a Theoretical Framework to Investigate Alignment of Big Data in Healthcare through a Social Representation Lens

Abstract

The aim of this paper is to develop a theoretical framework grounded in the literature, which can be used to explore the influence of big data on business-IT alignment in the healthcare context. Increasingly the availability of information systems in healthcare delivery and service management results in massive amounts of complex data that have the 3V characteristics of big data (i.e. volume, variety, velocity). Use of big-healthcare-data has been identified as bringing significant benefits to the healthcare sector from improved decision making through to population health analysis. Although the technical dynamics around big data such as analytics and infrastructure requirements are extensively researched, less attention has been given to social dynamics such as peoples’ experience, understanding and perceived usefulness of this data. To address this gap, the paper uses social representation theory as a methodological lens to develop a theoretical framework to study the social dynamics around big data and its use in the healthcare context. The selected case for this development is the New Zealand healthcare sector and an approach using multi-level macro, meso, and micro analysis is taken. Use of social representation theory as a methodological lens to develop a theoretical framework is a novel approach. Such a theoretical framework will be useful as a foundation for carrying out on-going empirical research on big data to understand its influence on business-IT alignment in the healthcare context.