Multi-Level Governance in Humanitarian Organizations: Implications for Information Flows and Data Analytics

As advances in national and organizational ICT infrastructure make rich data flows and more complex analytics possible, it is necessary to understand the factors that both facilitate and hinder appropriation of these technologies. While many analyses, particularly in the information and communication technologies for development (ICTD) literature, point to a variety of technical, project management, and goal misalignment factors as critical for success, the contributions of institutions and governance receive less attention. Also, when they are, analyses are largely constrained to national level policies. However, institutional and governance impacts on data flows and analytics can be observed across a variety of levels (global, national, sectoral, organizational and even group levels). It is also the case that governance effects spans levels, as well as geographic and organizational boundaries. To understand these effects, a multi-level, multi-organizational approach is needed. This research investigates the efficacy of this multi-level approach through a multi-organizational case study of the adoption and use of geographic information systems and their related geocoded data. GIS represent a positive case for inter-organizational data flows because their layered architecture typically relies on specialization across numerous sectors. For example, an online map may representing hospitals, schools, shopping centers, crime and air quality may be derived from five separate databases managed and controlled by five distinct agencies. This research on organizational use of GIS uses a novel analytic framework constructed from three complementary streams of institutional theory. From North’s stream of New Institutional Economics, a focus on higher level governance and its implications for societal level institutions is derived. From Scott’s Institutions and Organizations, concepts related to organizational and organizational field levels are integrated. Finally, from political science’s multi-level governance theory, concepts related to policy making and devolution of authority are borrowed. The framework facilitates institutional analyses of data flows and analytics. Of particular interest here are: How are information flows influenced by multi-level governance mechanisms? What are the outcomes of these mechanisms? How do multi-level governance mechanisms influence data analytics? and, How does multi-level governance affect an organization’s ability to introduce new analytic techniques? The framework’s validity is tested through a case study of the U.S. Agency for International Development’s (USAID) GeoCenter and use of GIS by its West Bank mission. USAID is an international humanitarian organization with many foreign operations, making it an ideal context for the study of multi-level governance, particularly in an international context. At the time of the research the organization has recently formed a new GeoCenter with a mission to expand the use of GIS and spatial analyses across the agency. USAID’s West Bank mission was identified as a lead user of GIS and hence had already developed many inter-organizational relations involving geocoded data flows. The case is based on in-depth Interviews with 20 managers at three relational levels, across two organizational levels, in 8 organizations. The organizations include USAID, both their Washington D.C. and Jerusalem offices, and their implementation and data sharing partners in the West Bank. Results indicate governance mechanisms have a variety of effects on data flows and analyses. First, the autonomy of the local offices, which exists to provide legitimacy, enables these offices to define their own data structures and innovate locally. In turn, the ability to define data ontologies and structures can facilitate data flows locally, but can fragment systems on a broader scale. Multi-level governance also has implications for data quality as well as volumes, which in turn can influence the types of analyses possible.