Information Sharing in and Across Government Agencies

Abstract

This article is based on an exploratory, interdisciplinary study of issues related to information sharing within and across three public agencies. Based on Schein's work, three subcultures within the public sector (scientist, politician, and bureaucrat) were identified as a framework to examine these issues. Dawes's three categories of benefits and barriers, associated with interagency information sharing (technical, organizational, and political), were also used in developing the framework. Their work has been extended by identifying three types of differences (view, use, and purpose) among these subcultural relationships to data and information. Four types of systems (social, constituency, technical, and organizational) that influence information-sharing processes within and across agencies also were identified. Two cases are offered to illustrate key points about information sharing across subcultures and some implications for research and practice to enhance abilities within the public sector to appropriately and effectively share information.N ew and existing information systems have significant potential to help government agencies address complex societal problems, but such systems are often stymied in their implementation and only partially able to deliver on their full promise. Traditional wisdom points to cultural differences between government agencies and a resulting lack of trust as key barriers to information sharing. A less well-understood barrier lies in the differences among subcultures within agencies. These subcultures grow out of their respective occupational communities (e.g., scientists, planners, and appointed leaders) and their unique languages, values, and developmental paths. The information-sharing needs and behaviors in an agency are driven as much by the characteristics of these subcultures as by the overall agency culture.

Key Differences of Private and Public Sector Business Process Change

“…Interdepartmental rivalries (at both political and administrative levels) and unintegrated information systems often hinder the cooperation and collaboration of different public departments (Bannister, 2001). In addition, public departments are often reluctant to give out data on past projects, because they are "frequently tied to funding, political agendas, legal guidance, and public demand, and the resulting data are increasingly used to justify or advocate for certain stances on policy, law, programs, and so on" (Drake, Steckler, & Koch, 2004). Furthermore, in order for public organizations to collaborate with each other requires information systems which support this exchange of ideas.…”

Disaster Response After Extreme Events in Taiwan: The Influence of Formal Institutions on Inter‐Organizational Interaction

“…Scholars have long explored the factors that influence the formation and evolution of inter-organizational interaction in disaster response systems (Comfort, 2007;Kapucu, 2007;Simo & Bies, 2007;Waugh & Streib, 2006). The political and policy perspective suggests that factors such as legislation and policy (Dawes, 1996;Sapat & Esnard, 2012;Siddiki, Carboni, Koski, & Sadiq, 2015;Yang & Wu, 2014), power and authority (Henning & Ng, 2009), and the political environment (Dawes, 1996;Drake, Steckler, & Koch, 2004;Han, Hu, & Nigg, 2011) influence inter-organizational interaction. Another line of inquiry, the organizational and managerial perspective, considers factors such as organizational structure, resources, culture, trust, and leadership (Ansell & Gash 2007;Crosby & Bryson, 2010;Graddy & Chen, 2006;Huxham & Vangen, 2004;Provan & Kenis, 2008).…”

An Architectural Approach to Analyze Information Quality for Inter-Organizational Service

“…In the class of applications that heavily depends on information quality (IQ), a typical approach to solve the IQ problem usually starts and ends with the activities scoped to the physical data storage level (Wang et al, 1995). But most of the efforts have only been successful to a certain degree (Drake et al, 2004). Given that in business applications information exists within the context of business processes, the attempts to solve IQ problems at the purely physical information level are not effective (Drake et al, 2004).…”

“…But most of the efforts have only been successful to a certain degree (Drake et al, 2004). Given that in business applications information exists within the context of business processes, the attempts to solve IQ problems at the purely physical information level are not effective (Drake et al, 2004). The physical level does not capture the requisite semantics to accurately communicate information across processes.…”