Big data as an emerging technology is generating a lot of interest in a number of industries worldwide. Firms in developing countries (DCs) have not been left out in the impact of this phenomenon. Examples of such firms are health insurance firms, who tend to collate health insurance data in large volumes, at high velocities, with variety and veracity to gain value from the data. Despite this promise, there is a lacuna of knowledge on how firms develop capabilities to leverage big data to their benefit. That is the purpose of this thesis – to examine how health insurance firms in DCs develop capabilities to leverage big data to their benefit.
From a critical realism perspective, this thesis used the dynamic capabilities theory and capability lifecycle (CLC) framework, to examine how health insurance firms in Ghana develop capabilities to leverage big data to their benefit. Ghana has been chosen as the research site because its health insurance industry exhibits firms who are attempting to leverage their large volumes of data for benefit. In this respect, three key objectives were pursued. The first objective sought to explore the typology of big data capabilities used by Ghanaian health insurance firms. The second objective sought to examine how Ghanaian health insurance firms are developing the typology of big data capabilities to leverage big data to their benefit. The third objective sought to examine the benefits Ghanaian health insurance firms obtain by leveraging big data. Using a case study, a qualitative research strategy, the thesis examined two Ghanaian health insurance firms who were in the process of implementing big data systems.
For the first objective, the research found that the typology of big data capabilities consist of a four level unidirectional hierarchy. Big data capabilities is at the apex of the hierarchy with the potential to give firms dynamic capabilities. Technological capabilities, human skills capabilities and organisational capabilities were found to follow as second-order (level) capabilities. Technological capabilities was found to be made up of infrastructural capabilities, data management capabilities and analytical capabilities as first-order (level) capabilities. Human skills capabilities was found to consist of IT skills capabilities, data science capabilities and business analytic capabilities as first-order (level) capabilities. The organisational capabilities were found to consist management capabilities, organisational cultural capabilities and data ethical capabilities as first-order (level) capabilities. The various first-order (level) capabilities were found to consist of various resources which act as the foundations for big data in the firms. At the foundation (zero-order/level), the resources were found to be entangled - complementing each other and having the ability to span across (or be critical to all) the first-order (level) capabilities.
Concerning the second and third objectives, a proposed big data capability evolution framework was used answer these objectives. Using the framework, the development of big data capabilities and its subsequent leveraging to derive benefits were found to have been driven by three strategic directions. The strategic directions were to, first, leverage data as a valuable asset which led to the development of the technological capabilities; second, leverage data as an intellectual asset which led to the development of the human skills capabilities; and lastly, leverage data as an organisational asset which led to the development of the organisational capabilities. The benefits derived in leveraging big data by the health insurance firms included IT infrastructure capability and flexibility through the acquisition of cloud computing which offers ease to dynamically allocate resources. Other benefits were cost reduction due to fraud detection and reduced medical adjudicated errors, creation of data-driven cultures to aid changing work patterns and business alliance support through linkages with industry stakeholders through the big data systems.
In a closer examination, eighteen (18) key findings were established. Notable among them are, first, DC firms in developing big data capabilities, circumvent their resource constraint by using cloud computing, offline to online (O2O) network technologies, open source software and outsourcing of scare human skills like data science skills, among others. Generally, DC firms due to their resource constraint take the path of least cost in leveraging new technologies like big data. These findings are consistent with previous studies in information systems (IS) in developing countries. Second, some big data capabilities (e.g. data acquisition capabilities, data science capabilities) are intrinsically developed as part of software applications. Thus, firms have to ‘own’ them through learning and strategically applying them to specific use. The learning process is coupled with organisational changes such as changes in work patterns and business processes. Further, the strategic application of big data may be influenced by level of domain (health insurance) knowledge and the environmental resources such as legal and institution frameworks in the industry. As such, the human skills and organisational capabilities tend to matter more in leveraging big data for benefit. These findings address the paucity of knowledge on how to develop capabilities and leverage big data for benefit.
For novelty and originality, this research has established a typology of big data capabilities which serves a model for both academics and practitioners on the constituents of big data in an organisational context. The research has also extended the CLC by merging it with the typology of capabilities to establish the big data capability evolution framework. This framework explains how big data capabilities are developed and leveraged into benefits. It has also provided insight into the application of critical realism in empirical IS research. The research has also contributed two new ‘V’ characteristics of big data namely vacuity and viewpoint. The thesis make a case for future research to focus on developing exploring the nature of big data capability development in a public sector institution – for example examining national health insurance schemes. This is pertinent since capability development research in information systems has primarily focused on private sector institutions.
The purpose of the study is to understand the enablers and constraints in work environment virtualisation in a developing country higher education context. The nature of work and work environment are rapidly changing in terms of where, how and when people work. Though globalisation and information technology have been key trends shaping this changing nature of the work environment, Information Systems (IS) research on HEIs systems over the years has focused more on e-learning and the virtual learning environment (VLE). As a result, less research attention has been given to virtualisation of work environment within HEIs. Also, whilst IS research in HEIs has examined technology virtualisation in terms of desktop virtualisation, server virtualisation and network virtualisation, less emphasis has been on work environment virtualisation or the virtualisation of the human work experience. It is thus important that IS research on higher education information systems pays attention not only to the learning environment but also to the work environment since the work environment offers the necessary support for learning. Moreover, previous research on activity theory and IS research have mainly focused on a single actor, a dyad of two subjects or a team but not as multiple actors in a principal-agent relationship engaged in an activity.
To address these gaps, the thesis employs an interpretive case study approach as the methodology and a combined lens of activity and agency theories as the theoretical foundation to understand how an HEI in a developing country context can migrate its physical work environment to virtual work environment. The findings show that the historical nature of the physical work environment, the inefficiency and delays it causes in the work environment can influence an HEI to virtualise its work environment. The findings also identify two levels of contradictions that pose as constraints in work environment virtualisation using an offshore agent. First, contradictions are evident at the HEI activity system level and at the principal-agent relationship level. The findings indicate that the virtualisation of work environment in HEIs using an external consultant relies on how the HEI and the external consultant work interactively as activity systems, how the contradictions within and between them are resolved and how they learn from these interactions. The findings show how contradictions caused by role conflicts, staff’s fear of elimination and external consultants’ limited understanding of rules and procedures of the HEI context can hamper work environment virtualisation. It shows further that a resolution of these contradictions can lead to a virtual work environment that provides the platform for better and efficient information management.
By employing activity and agency theories as a combined lens, the study offers a novel application of activity theory in work environment virtualisation. It is argued that activity theory can be extended with agency theory to explain contradictions within and between subjects in IS development and implementation. The study is limited by its single case perspective in one developing country. However, future research can compare the experience of different HEIs as well as from a developed country perspective in order to account for contextual differences. The study provides practitioners with insights on how to address the relationship between users, designers and implementers in IS development and implementation process. In particular, it addresses the critical issues in the migration process in terms of social rules, division of labour and community. The study is a first attempt to offer rich insight into how HEIs can virtualise its work environment through a contextual understanding of the principal-agent relationship.