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Considers alternative types of computer models designed to assist decision makers on questions of site location. Relates to a larger scale research project which aims to examine the necessary requirements for computers to provide practical decision support. It involves comparing a wide range of modelling techniques, with a view to establishing their relative strengths and the conditions under which each would be the most appropriate choice. Discusses the range of modelling techniques available and provides a more detailed review of one particular model which combines a “conventional” approach, based on algebraic equations, with an expert systems approach.

Originates from a research project which aims to investigate the potential for “intelligent” computer models as decision‐support tools, with particular emphasis on strategic analysis. Describes field work carried out with the intention of validating the working models which have been developed. The fieldwork produced a number of useful suggestions whereby the particular model could be enhanced, as well as demonstrating the potential for applying intelligent models in practice.

Neural Network (NN) simulation models are being increasingly utilised in the business and management fields as forecasting, pattern recognition and classification tools. Their growing popularity appears to emanate from the ability of NNs to approximate complex non‐linear relationships, via their capacity to represent latent combinations of unobservable variables in hidden layers. Although there is a growing business literature on the ability of NNs to predict various corporate outcomes (e.g., corporate failure), and to forecast time series data (e.g., share prices), they have yet to be fully evaluated by business academics on cross‐sectional data. This paper provides an overview of the NN modelling approach and compares the performance of NNs, relative to conventional OLS regression analysis, in predicting the cross‐sectional variation in corporate audit fees. The empirical results suggest that the NN models exhibit superior forecasting accuracy to their OLS counterparts, but that this differential reduces when the models are tested out‐of‐sample.

Reviews recent trends and developments in the use of Information Technology in the banking environment. Considers the more advanced software technologies and examines two specific applications. The first involves the use of expert systems techniques as decision support tools; the second applies neural network techniques in a model of consumer perceptions of ATM machines.

Considers the scope for using computer models to assist in tourism planning, with particular emphasis on the environmental issues. Suggests that a suitable platform for such models is the analytic hierarchy process which uses a tree structure to model both objectives and decision variables and is capable of handling subjective judgments and preferences.

Examines the ways in which computers can be used to assist in management decision making, emphasizing, above all, the practical aspects. Begins with the provision of management information, and goes on to consider the scope for bringing the computer into the decision itself. Discusses traditional operations research methods, as well as simulation techniques which are noted as being similar in principle to spreadsheet‐based models for planning and financial projections. Also considers knowledge‐based methods which handle qualitative rather than quantitative factors.

Examines the theoretical base of the neural network technique showing, for example, that it is linked in formal and practical terms with expert systems and that it uses a statistical approach. After beginning with a discussion of models of information processing by consumers, proceeds to outline the main features of the neural network approach including the important question of practical implementation. Then suggests a model to show how the neural network approach can be employed to shed light on the way in which consumers respond to stimuli contained in advertising messages, with particular emphasis on the stimuli which are implicit or embedded in these messages rather than being overtly measurable. Subjects the model and the neural network approach to critical evaluation, and suggests guidelines for further implementation.

Considers the scope for applying expert system techniques to assist in decisions regarding site location. Begins with a review of the theoretical literature and assesses the progress made in developing computer models. Its central theme is that the next step forward in this area is to develop expert systems. Addresses the question of the design of an expert system which would improve the decision‐making process. Concludes that such a system is well within the scope of current technology, as long as developers can avoid being overambitious and as long as the end result is highly user friendly.

Shows how neural networks can bring together psychometric and econometric approaches to the measurement of attitudes and perceptions. Uses a neural network to analyse data collected from a sample of ATM users on their perceptions of ATM service. Uses the weights of connections from input nodes to hidden nodes to label the hidden nodes to represent particular respondent attitudes. Uses the network to analyse the impact of explanatory (input layer) variables on the hidden layer attributes, and through these on the endogenous (output layer) variables ‐ satisfaction with ATMs, likelihood of recommendation to others, extent and frequency of use. Defines four user types, characterized as “disaffected youth”, “technophobes”, the “pro‐technology” segment, and the “cost conscious” segment. Gives some ideas on how banks could address the needs of each segment.

Describes work carried out at Cardiff Business School, University of Wales, to investigate the scope for applying Artificial Intelligence (AI) techniques in the field of management. The main thrust of the work so far has been oriented towards marketing decisions, but the implications extend to decision making in general.