Jay E. Aronson

Knowledge management: practices and challenges

Knowledge management (KM) is a process that deals with the development, storage, retrieval, and dissemination of information and expertise within an organization to support and improve its business performance. Organizations are realizing that knowledge is a crucial resource for organizations and it should be managed judiciously. Organizations need to harness knowledge not only to stay competitive, but also to become innovative. KM requires a major shift in organizational culture and a commitment at all levels of a firm to make it work. Through a supportive organizational climate, ideally, through effective KM, an organization can bring its entire organizational learning and knowledge to bear on any problem, anywhere in the world, at anytime

Knowledge management and the competitive strategy of the firm

Purpose – Knowledge management (KM) has emerged as a strategy to improve organizational competitiveness. Our purpose is to identify the relationship between KM and the firms competitive advantage.Design/methodology/approach – We review the existing literature on KM and strategy formulation. We utilize the resource‐based view approach as a lens for fitting KM with strategic planning. The concept of strategy and KM integration is introduced.Findings – We relate KM strategies to business strategy through a thorough review of the literature.Research limitations/implications – Future research should investigate the circumstances under which KM can create a sustainable competitive advantage within the framework of the RBV. Another area involves the investigation of the degree of integration between the competitive strategy and KM strategy.Practical implications – A very useful source of information for practitioners to refine their thinking about KM and the firm strategic resources. It offers the practitioner ...

Does UML make the grade? Insights from the software development community

The Unified Modeling Language (UML) has become the de facto standard for systems development and has been promoted as a technology that will help solve some of the longstanding problems in the software industry. However, there is still little empirical evidence supporting the claim that UML is an effective approach to modeling software systems. Indeed, there is much anecdotal evidence suggesting the contrary, i.e. that UML is overly complex, inconsistent, incomplete and difficult to learn. This paper describes an investigation into the adoption and use of UML in the software development community. A web-based survey was conducted eliciting responses from users of UML worldwide. Results indicate a wide diversity of opinion regarding UML, reflecting the relative immaturity of the technology as well as the controversy over its effectiveness. This paper discusses the results of the survey and charts of the course for future research in UML usage.

Research Report: The Effectiveness of Multiple Dialogues in Electronic Brainstorming

Members of brainstorming groups often pursue the same set of ideas rather than considering a wide and diverse range of ideas, which may reduce the number of ideas they produce. One way to reduce this cognitive inertia may be to encourage groups to engage in several simultaneous discussions or dialogues. This experiment, which studied groups brainstorming electronically, found that groups generated more ideas, more high-quality ideas, and more novel ideas when using multiple dialogues than when using single dialogues.

The impact of DSS use and information load on errors and decision quality

Abstract This paper uses a laboratory experiment to examine the effect of DSS use on the decision maker’s error patterns and decision quality. The DSS used in our experiments is the widely used Expert Choice (EC) implementation of the Analytic Hierarchy Process. Perhaps surprisingly, our experiments do not provide general support for the often tacit assumption that the use of a DSS such as EC improves decision quality. Rather, we find that, whereas a DSS can help decision makers develop a better understanding of the essence of a decision problem and can reduce logical error (especially if the information load is high), it is also susceptible to introducing accidental effects such as mechanical errors. In some cases, as in our study, the accidental errors may outweigh the benefits of using a DSS, leading to lower quality decisions.

People-to-People Lending: The Emerging E-Commerce Transformation of a Financial Market

This paper provides an overview of the concept of people-to-people (P2P) lending, a relatively new e-commerce phenomenon that has the potential to radically change the structure of the loan segment of the financial industry. P2P lending creates a marketplace of individuals and a social fabric through which these individuals interact. It provides efficient information transfer, thus perhaps creating more perfect markets. P2P lending requires information systems support to make it function, and to provide a social network mechanism that may be crucial for its success. We discuss different P2P lending marketplace models, and how information systems support the creation and management of these new marketplaces, and how they support the individuals involved. We conclude by providing some important research questions and directions, and issues for which further investigation is called.

An Interactive Reservoir Management System for Lake Kariba

Abstract This paper presents a user-interactive decision support system (DSS) for managing of the Lake Kariba reservoir. Built in the fourth-generation computer language IFPS, the system takes into account relevant reservoir characteristics and parameters, such as the amount of hydropower generated, reservoir storage throughout the year and the amount of water released for down-stream usage. The system blends water release rules determined previously using optimization and simulation-based scenario analyses with expert input from an experienced reservoir manager, yielding an intuitive and realistic DSS with which the reservoir manager may easily identify. The DSS also includes a Box-Jenkins time series model that forecasts future inflows. Each month, the system provides the manager with a proposed release schedule, which the manager then uses to explore and evaluate the consequences in terms of the decision criteria, over an extended period of time. The types of information provided to and sought from the manager correspond closely with actual reservoir management practice. An important characteristic of the system is that the manager can quickly explore various different potential release decisions a priori, for a variety of potential inflow scenarios, including predicted inflows for average hydrological years, as well as inflows reflecting extreme events such as drought and flood periods. The manager can compare the results of the release decisions made in the scenario analysis, both with the release strategy proposed by the system and with historical release decisions, thus aiding the manager in establishing effective reservoir management policies in practice. Therefore, rather than a mechanical value, our DSS offers the manager a flexible problem analysis with suggested courses of action. We illustrate the system using example sessions with an experienced reservoir manager. While the system is designed specifically to support the management of Lake Kariba, its extension to a more general class of reservoir management problems is straightforward.. 1782 1528 V 3

A computational study of empirical decision horizons in infinite horizon, multiperiod network flow problems

The minimum cost, multiperiod network flow model is an important optimization model for solving problems in many application areas including resource scheduling, planning and distribution. This network model describes decision making problems over time. In earlier work, we discussed the development, implementation and computational testing of a new technique, the forward network simplex method, for solving linear, minimum cost, multiperiod network flow problems. The forward network simplex method exploits the natural decomposition of multiperiod network problems, by limiting its pivoting activity to the date of the last few time periods. Both the solution CPU time and pivot count are linear in the number of time periods. For standard network optimization codes, the pivot count is linear while the solution time is approximately quadratic in the number of periods. Here we present a computational study of the natural decomposition of, or empirical decision horizons for, an “infinite” horizon, multiperiod net...

The Impact of Time Pressure on Idea Generation

The issues of time pressure and idea generation are vital concerns for businesses today. In this paper, we examine the impact of time pressure on idea generation and creativity. One hundred and two business students were used to examine the impact of time pressure on: (a) the rate of ideas generated, and (b) the rate of the creativity of the generated ideas.