Janusz Szczypula

Communication and information: alternative uses of the Internet in households

The Internet has been characterized as a superhighway to information and as a high-tech extension of the home telephone. How are people really using the Internet? The history of previous technologies that support interpersonal communication suggests that communication may be a more important use and determinant of participants’ commitment to the Internet than is information acquisition and entertainment. Operationalizing interpersonal communication as the use of electronic mail and information acquisition and entertainment as the use of the World Wide Web, we analyzed longitudinal data from a field trial of 229 individuals in 110 households during their first year on the Internet. The results show that interpersonal communication is a stronger driver of Internet use than are information and entertainment applications.

A brief history of balance through time

We present methods for establishing the amount of reciprocity, transitivity and group balance (a generalization of structural balance) in sociometric structures. These methods are applied to the second time series of sociometric data provided by the Newcomb (1961) study. The amount of reciprocity was above chance levels at the outset and showed no systematic variation thereafter. Transitivity has a very different time scale. It climbed steadily through the first nine weeks and remained stable thereafter. While consistent with chance at the beginning of the study period, it grew to above chance levels at week 3. Group imbalance declined throughout the entire period. The reasons for these different time scales are discussed.

Forecasting Analogous Time Series

Organizations that use time-series forecasting regularly, generally use it for many products or services. Among the variables they forecast are groups of analogous time series (series that follow similar, time-based patterns). Their covariation is a largely untapped source of information that can improve forecast accuracy. We take the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. In using Bayesian pooling, we use data from analogous time series as multiple observations per time period in a group-level model. We then combine estimated parameters of the group model with conventional time-series-model parameters, using so-called weights shrinkage. Major benefits of this approach are that it (1) requires few parameters for estimation; (2) builds directly on conventional time-series models; (3) adapts to pattern changes in time series, providing rapid adjustments and accurate model estimates; and (4) screens out adverse effects of outlier data points on time-series model estimates. For practitioners, we provide the terms, concepts, and methods necessary for a basic understanding of Bayesian pooling and the conditions under which it improves upon conventional time-series methods. For researchers, we describe the experimental data, treatments, and factors needed to compare the forecast accuracy of pooling methods. Last, we present basic principles for applying pooling methods and supporting empirical results. Conditions favoring pooling include time series with high volatility and outliers. Simple pooling methods are more accurate than complex methods, and we recommend manual intervention for cases with few time series.

Bayesian Hierarchical Forecasts for Dynamic Systems: Case Study on Backcasting School District Income Tax Revenues

Time series forecasting for relatedunits is common practice. Examples include sales of a chain of fast-food restaurants in a metropolitan area, precipitation in neighboring sections of farm land, and, as explored in this chapter, tax revenues for the school districts of a county. Our premise is that there is information in the cross- sectional data to be exploited for forecasting. The geographic scale of our research is much smaller and therefore our data are more dynamic than those of related studies. Garcia-Ferrer et al. (1987) and Zellner and Hong (1989) used Bayesian shrinkage methods for pooling time series forecasts — similar to the one we develop in this chapter — to forecast growth in the economies of several countries. Lesage and Magura (1990) applied the same and additional methods to multi-regional data, seven metropolitan areas in Ohio. Our interest is in small local government economies at the subregional level. We forecast (and backcast) income tax revenues for forty school districts in Allegheny County, Pennsylvania.

Barriers to information sharing in state agencies: the case of the texas general land office

This paper explores barriers to information sharing and how they might be overcome. Through a case study of the Texas General Land Office, computer-based information sharing is explored in the organizational setting. In particular, the State Real Property Inventory information system is examined in terms of utilization of hardware, software, and local area networks. In addition, managerial and user attitudes towards computer use are explored. We find a number of technological and organizational factors that need to be addressed in order to improve computer- based information sharing. These technological and organizational barriers combine to create an inefficient environment for adoption and use. Finally, we suggest some improvements on how management and users can utilize information sharing technologies in more effective ways in the context of structural integration of information technology.