|Although researchers and practitioners across various disciplines, including computer science, healthcare informatics, and clinical medicine have advocated that business analytics have tremendous benefits for healthcare industries, extant research has paid insufficient attention on the exploration of its business value. The series of essays in this dissertation strive to close this knowledge gap.
Essay 1 develops a generic IT-enabled transformation model based on resource-based theory and practice-based view. This model reveals the causal relationships among IT capability, IT-enabled transformation practice, benefit dimensions and business value. This proposed model is tested by analyzing secondary data consisting of big data analytics implementation cases in the healthcare context. Through analyzing these cases, this study seeks to understand better how healthcare organizations can leverage big data analytics for improving clinical practices and creating business value. In addition to conceptually defining four big data analytics capabilities, this model also identified three significant path-to-value chains which offer some insights regarding theoretical and managerial implications
Essay 2 investigates whether organizations’ decision making effectiveness can be influenced by the use of business analytics systems. Specifically, this works develops a research model to examine the mechanisms by which business analytics capabilities (i.e., effective use of data warehouse tools, effective use of analytics tools, and effective use of data visualization tools) in healthcare units are shown to indirectly influence decision-making effectiveness through a mediating role of absorptive capacity. This study employed a survey method to collect primary data from Taiwan's healthcare industry. Structural equation modeling (SEM) was used for path analysis. This study conceptualizes, operationalizes, and measures the business analytics (BA) capability as a multi-dimensional construct formed by capturing the functionalities of BA systems in healthcare. The result found that healthcare units are likely to obtain valuable knowledge as they utilize the data interpretation tools effectively. Also, the results show that the effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.
Essay 3 proposes a novel research model drawing on configuration view for the determination of business analytics-enabled business value. We examine how big data analytics capabilities interact with complementary organizational resources and organizational capabilities into multiple configurations to achieve quality of care and financial performance in hospital settings. To account for the holistic, equifinal, and complex interactions among business analytics elements needed to achieve business value, this study employs a relatively new approach termed fuzzy-set qualitative comparative analysis (fsQCA) that go beyond simple linear additive (or multiplicative) effects. The findings from fsQCA advance our understanding of how big data analytics-enabled IT capabilities combine with other organizational elements to achieve quality of care in health care. Most importantly, we offer evidence that different solutions leading to the same quality of care performance from the effective use of big data analytics and other organizational elements do exist.