Knowledge that Transforms

Conceptual design is an important, but difficult, phase of systems development. Analysis patterns can greatly benefit this phase because they capture abstractions of situations that occur frequentl...

Measurement is perhaps the most difficult aspect of behavioral research. In a recent edition ofISR, a scale for consensus on appropriation was developed. Consensus on appropriation is one of three global constructs incorporated in adaptive structuration theory (Poole and DeSanctis 1990). The principal components analysis on the initial questionnaire revealed two factors with eigenvalues greater than one. While the methods used to develop the scale were thorough, the weaker factor was excluded from the rest of the analysis with little justification. We suggest that this finding has two possible explanations, multidimensionality or response bias. This research note suggests that in addition to the convergent and discriminant validity that Salisbury et al. (2002) provided for the consensus on appropriation scale, we may have an opportunity to further refine the measurement of this construct. By further exploring this principal component finding, consensus on appropriation may be better understood and measured.

Conceptual design is an important, but difficult, phase of systems development. Analysis patterns can greatly benefit this phase because they capture abstractions of situations that occur frequently in conceptual modeling. Naïve approaches to automate conceptual design with reuse of analysis patterns have had limited success because they do not emulate the learning that occurs over time. This research develops learning mechanisms for improving analysis pattern reuse in conceptual design. The learning mechanisms employ supervised learning techniques to support the generic reuse tasks of retrieval, adaptation, and integration, and emulate expert behaviors of analogy making and designing by assembly. They are added to a naïve approach and the augmented methodology implemented as an intelligent assistant to a designer for generating an initial conceptual design that a developer may refine. To assess the potential of the methodology to benefit practice, empirical testing is carried out on multiple domains and tasks of different sizes. The results suggest that the methodology has the potential to benefit practice.

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.

Traditional development of large-scale information systems is based on centralized information processing and decision making. With increasing competition, shorter product life-cycle, and growing uncertainties in the marketplace, centralized systems are inadequate in processing information that grows at an explosive rate and are unable to make quick responses to real-world situations. Introducing a decentralized information system in an organization is a challenging task. It is often intertwined with other organizational processes. The goal of this research is to outline a new approach in developing a supply chain information system with a decentralized decision making process. Particularly, we study the incentive structure in the decentralized organization and design a market-based coordination system that is incentive aligned, i.e., it gives the participants the incentives to act in a manner that is beneficial to the overall system. We also prove that the system monotonically improves the overall organizational performance and is goal congruent.