Despite impressive technical advances in tools and methodologies and the organizational insights provided by many years of academic and business researches, the underperformance of information technology (IT) remains.In the past and even today, organizations experience difficulty in managing technology, changing from system to system, implementing new technology, maintaining compatibility with existing technologies, and changing from one business process to another. These problems impact significantly on business performance and will continue to do so if not addressed. As a result, many organizations have deployed EA in an attempt to address these challenges. However, the design and development of EA has proven to be easier than its institutionalization. The study explored the development and implementation of EA to determine the factors, which are barriers to its institutionalization. Two case studies were conducted.

Ontologically, what we do know is that healthcare big data do exist across South African health facilities. Epistemologically, the patients and some health practitioners do not know how the big data exist and, most importantly, what to expect from the data sets. This is due to the rawness and complexity of big data evolution, which many health practitioners do not know how to socio-technically analyze, and get an understanding of why things happen in the way that they do within patients’ big data. Thus, the objective of this study was to develop a framework, which could be used to guide analysis that can translate and transform big data into a more useful and purposeful resource for health practitioners, thereby improving services that they provide to the patients. The qualitative approach methods were employed, following the interpretivist approach. Existing literature relating to the study was gathered. Actor network theory was applied as a lens to guide the analysis of the data. Based on the analysis, a model and framework were developed. The model creates an analysis platform that enacts the use of actor network theory (ANT) as lens for data analysis. ANT offers a different type of analysis through its focus on the relational effect that is shaped by interaction that happen between humans and non-humans in their heterogeneous networks. The framework provides a guide for the transformation of healthcare big data from ontological to epistemological positions, towards improving patients’ care. Through the use of ANT for analysis and transformation of healthcare big data, the challenges of analytics of healthcare big data in technological, social, and policy barriers can be addressed, making this study useful to both the health profession and academia.

Purpose: The paper was intended to demonstrate the use and order of a combined lenses of two theories in IS research. It helps to understand how theories could be adopted in the order of methodological value. The way data are collected, organised and analysed is influenced and shaped by the order of use of the underpinning theories. The importance of the order is to create consistency, predictability, and uniformity of analysis, which have impact on the findings.Design/methodology/approach: Review of literature, and teaching and supervision experiences were used in the approach.Findings: The result of a combined use of both ST and ANT in the same study has been less than expected. The application of theories helps exhume findings. This makes the order‐of‐use of application of the theories significant. Some studies applied ST before ANT, and vice versa. There has never been a model or framework which defines the order‐of‐use of the theories. It could be argued that it depend on the nature and objectives of the study. This paper focuses on order‐of‐use of combined theories in information systems studies. No organisation has total power to determine what the choice(s) of an actor will be in a particular situation. Actor and structure by virtue of their interaction recursively produces and reproduces, on the one hand and on another, the actor and structure enable and at the same time there are constraints.Originality/value: Both theories can be used as a way of thinking and speaking about the phenomena being studied and also, as a “lens” through which the data is viewed and interpreted.

In the current climate of global competitiveness, many organisations are increasingly dependent on their IT strategy—either to increase their competitiveness, or often just to survive. Yet little is known about the non-technical influencing factors (such as people) and their impact on the development and implementation of IT strategy. There would therefore seem to be prima facie evidence that there is a need for a new approach to examining the relationships between social factors, technology and the organisation with respect to the development and implementation of IT strategy. This article seeks to make a contribution in this regard. Structuration Theory and Actor-Network Theory were employed to analyse how non-technical factors influence IT strategy. Structuration Theory holds that human actions are enabled and constrained by structures. Structures are rules and resources that do not exist independently of human action, nor are they material entities. Giddens describes them as ‘traces in the mind’ and argues that they exist only through the action of human beings. Actor Network Theory (ANT) provides a fresh perspective on the importance of relationships between actors that are both human and non-human. By their very presence, actors work to establish, maintain and revise the construction of organisational networks of aligned interests and gradually form stable actor-networks. ANT emphasises the heterogeneous nature of actor-networks which consist of and link together both technical and non-technical elements.