Organizations today face increasing pressures to integrate their processes across disparate divisions and functional units, in order to remove inefficiencies as well as to enhance manageability. Process integration involves two major types of changes to process structure: (1) synthesizing processes from separate but interdependent subprocesses, and (2) decomposing aggregate processes into distinct subprocesses that are more manageable. We present an approach to facilitate this type of synthesis and decomposition through formal analysis of process structure using a mathematical structure called a metagraph.

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a significant percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory (cf. McKeen et al. 1994, Weill and Olson 1989). In our survey of such studies, the majority failed to either detect or provide an estimate of the effect size. In cases where effect sizes are estimated, the numbers are generally small. These results have led some researchers to question both the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of interaction effects by accounting for the measurement error that attenuates the estimated relationships. The capacity of this approach at recovering true effects in comparison to summated regression is demonstrated in a Monte Carlo study that creates a simulated data set in which the underlying true effects are known. Analysis of a second, empirical data set is included to demonstrate the technique's use within IS theory. In this second analysis, substantial direct and interaction effects of enjoyment on electronic-mail adoption are shown to exist.

Data Quality Information (DQI) is metadata that can be included with data to provide the user with information regarding the quality of that data. As users are increasingly removed from any personal experience with data, knowledge that would be beneficial in judging the appropriateness of the data for the decision to be made has been lost. Data tags could provide this missing information. However, it would be expensive in general to generate and maintain such information. Doing so would be worthwhile only if DQI is used and affects the decision made. This work focuses on how the experience of the decision maker and the available processing time influence the use of DQI in decision making. It also explores other potential issues regarding use of DQI, such as task complexity and demographic characteristics. Our results indicate increasing use of DQI when experience levels progress through the stages from novice to professional. The overall conclusion is that DQI should be made available to managers without domain-specific experience. From this it would follow that DQI should be incorporated into data warehouses used on an ad hoc basis by managers.

This paper investigates the persistence of managerial expectations in an IT outsourcing context where the traditional relationship between supervisor and subordinate changes to one of client-manager and contractor. A mixed-method approach was used, in which a qualitative methodology preceded a large-scale quantitative survey. Data were collected from 147 survivors of a government IT organization which had undergone IT outsourcing in the previous year. Findings show that role overload, the presence of strong ties between manager and contractor, and the lack of prior outsourcing experience increased the persistence of managerial expectations. In turn, persistence of expectations had a distinct influence on managerial perceptions of contractor performance.