Alignment of big data perceptions in New Zealand healthcare : a thesis presented in partial fulfilment of the requirement for the degree of Doctor of Philosophy in Management at Massey University, Albany, New Zealand. EMBARGOED until 1 May 2021.
The growing use of information systems (IS) in the healthcare sector, on top of increasing patient
populations, diseases and complicated medication regimens, is generating enormous amounts of
unstructured and complex data that have the characteristics of ‘big data’. Until recent times data
driven approaches in healthcare to make use of large volumes of complex healthcare data were
considered difficult, if not impossible, because available technology was not mature enough to handle
such data. However, recent technological developments around big data have opened promising
avenues for healthcare to make use of its big-healthcare-data for more effective healthcare delivery,
in areas such as measuring outcomes, population health analysis, precision medicine, clinical care and
research and development.
Being a recent IT phenomenon, big data research has leaned towards technical dynamics such as
analytics, data security and infrastructure. However, to date, the social dynamics of big data (such as
peoples’ understanding and their perceptions of its value, application, challenges and the like) have
not been adequately researched. This thesis addresses the research gap through exploring the social
dynamics around the concept of big data at the level of policy-makers (identified as the macro level),
funders and planners (identified as the meso level), and clinicians (identified as the micro level) in the
New Zealand (NZ) healthcare sector. Investigating and comparing social dynamics of big data across
these levels is important, as big data research has highlighted the importance of business-IT alignment
to the successful implementation of big data technologies.
Business-IT alignment is important and can be investigated through many different dimensions. This
thesis adopts a social dimension lens to alignment, which promotes investigating alignment through
people’s understanding of big data and its role in their work. Taking a social dimension lens to
alignment fits well with the aim of this thesis, which is to understand perceptions around the notion
of big data technologies that could influence the alignment of big data in healthcare policy and
practice. With this understanding, the research question addressed is: how do perceptions of big data influence alignment across macro, meso, and micro levels in the NZ healthcare sector? This thesis is by
publication with four research articles that answer these questions as a body of knowledge.
A qualitative exploratory approach was taken to conduct an empirical study. Thirty-two in-depth
interviews with policy makers, senior managers and physicians were conducted across the NZ
healthcare sector. Purposive and snowball sampling techniques were used. The interviews were
transcribed verbatim and analysed using general inductive thematic analysis. Data were first analysed
within each group (macro, meso, and micro) to understand perceptions of big data, then across groups
to understand alignment. In order to investigate perceptions, Social Representations Theory (SRT), a
theory from social psychology, was used as the basis for data collection. However, data analysis led to
the decision to integrate SRT with Sociotechnical Systems Theory (SST), a well-known IS theory. This
integration of SRT with SST developed the Theory of Sociotechnical Representations (TSR), which is a
key theoretical contribution of this research. The thesis presents the concept and application of TSR,
by using it to frame the study’s findings around perceptions of big data across macro, meso and micro
levels of the NZ healthcare sector.
The practical contribution of this thesis is the demonstration of areas of alignment and misalignment
of big data perceptions across the healthcare sector.--Shortened abstract