Impact of Organizational Factors on Big Data Analytics Adoptions in U.S. Public Sector Organizations

Although big data analytics (BDA) have demonstrated the ability to increase productivity when successfully adopted, organizations of all types still struggle to achieve sufficient value to offset the costs of BDA adoption. In the public sector, innovative technology adoption always carries political risk, as neither political overseers nor the media nor the public will tolerate failed projects that may be viewed as irresponsible or wasteful misuses of public resources. While substantial research has been conducted on the technical aspects of BDA adoption, little research has been conducted on the organizational aspects. Furthermore, the small amount of research conducted on organizational factors occurred in the private sector and was constrained to if the organizational context influenced adoption outcomes rather than how the organizational context influenced outcomes. The purpose of this parallel, nested mixed-methods study is to provide a deep exploration of how and why varying forms of organizational architecture impact the outcomes of BDA adoption in U.S. public sector organizations. The theoretical significance of this ongoing research will be to fill a gap in the literature exploring the impact of organizational forms on BDA adoptions as a system, generally, and the dearth of research on public sector organizations, specifically. The practical contribution of this research will be to reduce uncertainty associated with organizational factors, allowing public sector managers to make risk-informed decisions on BDA adoption. Better informed decisions on BDA adoption should reduce the incidence rate of organizations failing to achieve sufficient public value to justify adoption costs and ease the burden on taxpayers and nonprofit donors caused by failed public sector BDA adoptions. This ongoing research will be complete in Spring 2022.