Mr. John Serbe Marfo, a student from the Department of Operations & Management Information Systems (OMIS), on 19th September 2017, successfully presented and defended his PhD thesis at the University of Ghana School of Graduate Studies. The thesis was on the topic; “Developing Capabilities to Leverage Big Data for Health Insurance: Evidence from Ghana”. His supervisors were Prof. Richard Boateng and Dr. John Effah.
Mr. Serbe Marfo’s study sought to develop a typology of big data capabilities that explains how firms leverage big data to obtain benefits. This was projected in his objectives, one of which was to explore the typology of big data capabilities being used by Ghanaian health insurance firms to leverage big data to obtain benefits. The study was also aimed at examining how Ghanaian health insurance firms develop the typology of big data capabilities to leverage big data to obtain benefits.
He expects his study to contribute to knowledge by extending the application of the capability lifecycle (CLC) framework to big data and the health insurance sector.
Below is the Abstract of Mr. Serbe Marfo’s thesis.
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 is 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 nature of big data capabilities used by Ghanaian health insurance firms. The second objective sought to examine how Ghanaian health insurance firms are developing 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 nature of big data capabilities (objective one), the findings suggest a typology of big data capabilities which depicts a four-level unidirectional hierarchy to conceptualising big data capabilities. Big data capabilities were found to exist 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 were found to be made up of infrastructural capabilities, data management capabilities and analytical capabilities as first-order (level) capabilities. Human skills capabilities were 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, 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 thesis makes 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.
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