According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Cultivating Communities of Practice: A Guide to Managing Knowledge

The Silent Language

Objective:Our objective was to quantify incremental risk associated with transfusion of packed red blood cells and other blood components on morbidity after coronary artery bypass graftingDesign:The study design was an observational cohort studySetting:This investigation took place at a large tert

Morbidity and mortality risk associated with red blood cell and blood-component transfusion in isolated coronary artery bypass grafting.

Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neural network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selection and system architecture design have been widely researched, but the corresponding question of how much information to use in producing high-quality neural network models has not been adequately addressed. In this paper, the effects of different sizes of training sample sets on forecasting currency exchange rates are examined. It is shown that those neural networks-given an appropriate amount of historical knowledge-can forecast future currency exchange rates with 60 percent accuracy, while those neural networks trained on a larger training set have a worse forecasting performance. In addition to higher-quality forecasts, the reduced training set sizes reduce development cost and time.

An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks

Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance

Purpose – To propose and evaluate a novel management structure that encourages knowledge sharing across an organization.Design/methodology/approach – The extant literature on the impact of organizational culture and its link to management structure is examined and used to develop a new knowledge sharing management structure. Roadblocks to implementing a new management structure and methods for overcoming these impediments are discussed. The efficacy of the proposed management structure is evaluated empirically by examining its effect on organizations that have implemented portions of the proposed structure.Findings – The foundational ideas behind the proposed knowledge management organizational structure and the structure itself have been implemented in parts at various organizations located both in the USA and internationally. While the full management structure model has not been evaluated, the portions implemented in various organizations have enabled these organizations to assume leading roles in thei...

Organizational Knowledge Management Structure

Artificial neural networks were used to support applications across a variety of business and scientific disciplines during the past years. Artificial neural network applications are frequently viewed as black boxes which mystically determine complex patterns in data. Contrary to this popular view, neural network designers typically perform extensive knowledge engineering and incorporate a significant amount of domain knowledge into artificial neural networks. This paper details heuristics that utilize domain knowledge to produce an artificial neural network with optimal output performance. The effect of using the heuristics on neural network performance is illustrated by examining several applied artificial neural network systems. Identification of an optimal performance artificial neural network requires that a full factorial design with respect to the quantity of input nodes, hidden nodes, hidden layers, and learning algorithm be performed. The heuristic methods discussed in this paper produce optimal or near-optimal performance artificial neural networks using only a fraction of the time needed for a full factorial design.

Heuristic Principles for the Design of Artificial Neural Networks

This study measures the value of website quality in terms of its impact on trust, intention to transact and price premiums. Prior research on online auctions has focused on the use of reputation systems for building trust in online auction vendors and subsequently to generate price premiums. This study examines the extent to which trust can be induced by improving the quality of online auction listings. A survey of 701 eBay users is conducted which compares the price premiums of two nearly identical online auction businesses, one that has online auction listings with a perceived high quality and the other that has substantially lower perceived quality. Results of this study indicate that website quality can explain 49% of the variation in the trust for eBay sellers. In fact, it shows that sellers with good website quality are all perceived to be equally trustworthy regardless of their eBay reputation; whereas sellers with poor website quality are not perceived to be trustworthy even if they have a high eBay reputation score. The results also show that the trust resulting from increased website quality increases intention to transact and results in price premiums of 12% (on average) for sellers with higher quality listings. Theories from marketing, economics, and social psychology are used to explain why website quality induces trust in unknown vendors without providing any concrete evidence regarding the vendor's past history.

Steven Walczak

Papers

An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks

Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance

Organizational Knowledge Management Structure

Heuristic Principles for the Design of Artificial Neural Networks

The relationship between website quality, trust and price premiums at online auctions