Recently, researchers have begun investigating an emerging, technology-enabled innovation that involves the use of intelligent software agents in enterprise supply chains. Software agents combine and integrate capabilities of several information technology classes in a novel manner that enables supply chain management and decision making in modes not supported previously by IT and not reported previously in the information systems literature. Indeed, federations and swarms of software agents today are moving the boundaries of computer-aided decision making more generally. Such moving boundaries highlight promising new opportunities for competitive advantage in business, in addition to novel theoretical insights. But they also call for shifting research thrusts in information systems. The stream of research associated with this article is taking some first steps to address such issues by examining experimentally the capabilities, limitations, and boundaries of agent technology for computer-based decision support and automation in the procurement domain. Procurement represents an area of particular potential for agent-based process innovation, as well as reflecting some of the greatest technological advances in terms of agents emerging from the laboratory. Procurement is imbued with considerable ambiguity in its task environment, ambiguity that presents a fundamental limitation to IT-based automation of decision making and knowledge work. By investigating the comparative performance of human and software agents across varying levels of ambiguity in the procurement domain, the experimentation described in this article helps to elucidate some new boundaries of computer-based decision making quite broadly. We seek in particular to learn from this domain and to help inform computer-based decision making, agent technological design, and IS research more generally.
Knowledge represents a critical resource in the modern enterprise. But it is dynamic and distributed unevenly. Capitalizing on this dynamic resource for enterprise performance depends upon its rapid and reliable flows across people, organizations, locations, and times of application. From a technological perspective, this points immediately to the design of information systems to enhance knowledge flows. The problem is, the design of information systems to enhance knowledge flows requires new understanding. The research described in this paper concentrates on understanding the dynamics of knowledge phenomenologically and on developing and applying techniques for modeling and visualizing dynamic knowledge flows and stocks. We draw key, theoretical concepts from multiple literatures, and we build upon integrative modeling work that composes a parsimonious, multidimensional, analytical framework for representing and visualizing dynamic knowledge. We then conduct field research to learn how this theoretical framework may be used to model knowledge flows in practice. By focusing this empirical work on an extreme organization and processes that involve and rely upon tacit knowledge, we illustrate how dynamic knowledge patterns can inform design in new ways. New chunks of kernel theory deriving from this fieldwork are articulated in terms of a propositional model, which provides a basis for the development of testable design theory hypotheses.
Today, a second generation of computer-based reengineering tools employs knowledge systems technology to automate and support key intellectual activities required for effective process redesign. But a central question remains as to the effectiveness of redesign through such knowledge systems. The research described in this paper is focused on testing the effectiveness of knowledge-based, process-redesign systems. We employ one such system, called "KOPeR-lite," as a platform for experimentation to assess the relative efficacy of redesigns generated by computer versus those developed by people. In this sense, we conduct a modified Turing Test to compare redesign performance of reengineering analysts with that of the knowledge system. KOPeR-lite performs comparatively well in certain respects, but human subjects outperform the machine in others. The results provide evidence to support claims of redesign efficacy through knowledge systems, and they offer insight into the relative strengths and weaknesses of people and software applications in the reengineering domain. This study further opens up new lines of research and highlights implications for process redesign and practice, including issues associated with leading adoption of knowledge system technology and extension of redesign automation systems such as KOPeR-lite.
This first decade of business process reengineering (BPR) is blemished by sporadic success, pathological performance, and inefficiency. Reengineering inefficiency is driven in part by cost and cycle time for process redesign, a process itself that requires deep reengineering knowledge and specialized expertise. However, such knowledge and expertise are not addressed by extant, first-generation redesign tools, so these intellectual activities must be performed manually at present, or provided through expensive BPR consulting services. Knowledge-based systems (KBS) address the requirements for knowledge and expertise directly, and they can augment first-generation tools to reduce redesign cost and cycle time, and hence increase reengineering efficiency. This study employs the methods and tools of reengineering recursively, to redesign the process of process redesign itself. Using measurement-driven inference, a second-generation KBS redesign tool called KOPer.