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Social media applications are slowly diffusing across all levels of government. The organizational dynamics underlying adoption and use decisions follow a process similar to that for previous waves of new information and communication technologies. The authors suggest that the organizational diffusion of these types of new information and communication technologies, initially aimed at individual use and available through markets, including social media applications, follows a three‐stage process. First, agencies experiment informally with social media outside of accepted technology use policies. Next, order evolves from the first chaotic stage as government organizations recognize the need to draft norms and regulations. Finally, organizational institutions evolve that clearly outline appropriate behavior, types of interactions, and new modes of communication that subsequently are formalized in social media strategies and policies. For each of the stages, the authors provide examples and a set of propositions to guide future research.

Policy researchers have become increasingly familiar with a number of improved techniques for analyzing data obtained from interrupted time‐series designs for evaluating public programs and policies. In this paper we contribute to this trend by presenting two groups of data analysis techniques which are not currently widely used by policy researchers, but are likely to be valuable adjuncts to traditional regression techniques for analyzing data obtained from interrupted time‐series designs. First, aids for model specification are presented that enable the analyst to define an appropriate linear trend model—often one which will reduce the degree of multicollinearity and, therefore, produce more precise estimates of the impacts of a public program or policy. Next we consider approaches for point estimation and joint (simultaneous) interval estimation of a policy intervention's total effect at various points in time.

In general, procedures for the analysis of interrupted time series are quite sophisticated and powerful. However, procedures for identifying the intervention component of inter rupted time-series models remain relatively primitive. In this article we demonstrate how exponential smoothing can play a function in the identification of the intervention component of an interrupted time-series model that is analogous to the function that the sample autocorrelation and partial autocorrelation functions serve in the identification of the noise portion of such a model.

ABSTRACTDecision support for managers and policy makers, such as required in planning and evaluation efforts, often requires ad hoc behavioral modeling to account for context‐specific phenomena and to handle data limitations. This paper introduces a systematic approach useful for meeting these requirements in which time‐varying parameter estimation plays an important role. A case study evaluating the impacts of public policy actions on residential natural gas conservation illustrates the approach.

ABSTRACTIn recent years, time series analysts have shifted their interest from univariate to multivariate forecasting approaches. Among them, the Box‐Jenkins transfer function process and the state space method have received the most attention. This paper presents a simplified approach that embodies some desirable features of existing methods. It stresses empirical analysis, has a unified modeling structure, is easily applicable, and is adaptive to changes without necessitating prior information on the evolution of a system under study. The core of the method relies on the Carbone‐Longini adaptive estimation procedure (AEP). Results of a comparative study based on the well‐known Lydia E. Pinkham data and the Box‐Jenkins sales/leading indicator data illustrate the merits of multivariate AEP in improving forecasting accuracy while simplifying the analysis process.Subject Area: Forecasting.