Cheol H. Oh

Explaining use of information in public policymaking

In recent years, scholars have attempted to understand the role of information in policymaking by developing several models of information utilization and have tested them empirically, at both national and state levels. This paper has called into question past studies as they relate to describing and explaining use of information. This paper tests an integrated model of information utilization that contains four sets of primary variables: decision makers’ environments (i.e., nature of policy issues), organization, individual characteristics, and characteristics of information. Based on the conceptual framework, a path model is built and tested against data about knowledge utilization and policy change in two areas of mental health policy (i.e., service and financing). The findings of the study have demonstrated that decision makers’ behavior does not conform to the assumptions put forward by either the organizational interest (e.g., information source or content of information) or the communications perspective (e.g., decision makers’ attitudes toward social science research). Instead, we have shown that information utilization is affected directly and indirectly by a variety of factors and their linkage, and not dominated by one set of factors that is defined by a singe perspective. The most important paths in the model are those between factors associated with information (e.g., the amount of information received or information source) and the use of information. Interestingly, these factors also play the role of major intervening mechanisms for linking other factors to decision makers’ use of information. More importantly, the general pattern of the findings indicates that policy areas make a difference in the process of information utilization.

Explaining the impact of policy information on policy-making

This article has called past studies into question as they relate to describing and explaining the impact of information on policy-making. More specifically, it attempts to empirically investigate the causality of the factors involved in the impact of information on governmental decision-making. Based on an integrated conceptual framework for when and how information helps to make policy decisions, a path model (or a covariance structure model without latent variables) is built and tested against the data in two areas of mental health policy (i.e., service provision and financing). Findings of the study demonstrate that how and when information influences governmental decision-making is directly and indirectly affected by a variety of factors and their linkages, not dominated by one set of factors (e.g., trustworthiness of information source or format of reports) defined by a single perspective (e.g., the organizational interest or the communications perspective). The most important paths in the model are those between factors related to information (e.g., the amount of information received or its use) and the impact of information on policy-making. Interestingly, these factors also play a major role in linking other variables (e.g., demographics or decision makers’ distrust of information) to the impact of information on policy-making. Furthermore, the general pattern of the findings indicates that policy areas make a difference in accounting for the impact of information on policy-making. Overall, the single most important lesson is that past perspectives are not alternative or competing tools for understanding the phenomena, and, thus, the theoretical and/or empirical task of explaining when and why information affects policy-making is equivalent to explaining why a certain set of factors is not appropriate or appropriate for a particular context and to identifying such a context.

System Size and Administrative Component in the American States: A Longitudinal Analysis of Economies of Scale Hypothesis

This study challenges economies of scale hypothesis as a useful way of explaining the effects of increasing system size on the administrative component of systems at the societal level (e.g., states). The proposition to be tested is the inverse relationship between state population and the relative size of government. More importantly, this study examines whether the relationship observed in cross-sectional studies can also be obtained from a longitudinal design. I collected data about the population and government employees of the American states from 1952 to 1990 and analyzed them by trend and time-series analyses. Findings show that the size of government grows at a faster rate than the increasing population of the states. These findings indicate that the different result is not simply a matter of how to define concepts (e.g., the administrative component in the states), but of how to model the basic process underlying the relationship between population and government structure. They also suggest that coordination and complexity hypothesis may be more promising at the societal level. Furthermore, I discuss the assumed isomorphism between organizations and social systems (e.g., states).