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Expert systems (ES) design emulates expertise with the intention that the ES be used by non-experts. This study aims to predict end-user intention and use of ES by proposing a research model that extends the basic components of the unified theory of acceptance and use of technology (UTAUT) by including additional relevant factors to ES, including the expert domain, perceived relevance, reliability, quality of ES and management support, which directly and indirectly influence the end-user intention to use an expert system.

A structural equation model (SEM), using LISREL, was used to test the measurement and structural models using a sample of 205 end-users of expert systems in the USA. These users of expert systems come from a variety of domains. The factors include both internal and external factors for the individual level of analysis design of this study.

The results showed behavioral intention had the strongest effect on usage, followed by perceived relevance. With respect to the factors that impact intention, perceived relevance had the strongest total effect, followed by attitude. For attitude, effort expectancy had the strongest total effect, followed by management support and perceived relevance.

The results of this study should assist decision-makers in planning training and communications about the use of expert systems so that the expert systems will be used as intended.

The originality of this work resides in the addition of external factors to the UTAUT model that helps provide advice to practitioners in the support needed to insure expert system implementation success.

Intelligent solutions, based on expert systems (ES), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. Expert systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning and its explanation capabilities. Provides an overview for the operations researcher of the expert systems methodology, as well as their historical and current use in business. Aims to present and focus on the wide range of business areas of ES applications, avoiding an in‐depth analysis of all the applications ‐‐ with varying success ‐‐ recorded in the literature. Reveals that, although still regarded as a novel methodology, ES are shown to have matured to the point of offering real practical benefits in many of their applications.

As technological progress races on, the Library and Information Science (LIS) profession is continually faced with new challenges and new technologies to aid in the effective dissemination and use of information. This paper looks at the challenge and implications of expert systems technology for LIS. It will discuss in particular the possible contribution of LIS to expert systems development, in the light of a recent survey of 50 expert systems producers in the United Kingdom, conducted by the authors. It concludes that there is room and need for LIS skills in expert systems development teams, but that these skills may need to be augmented by more specific computing experience if LIS graduates are to have a realistic chance of employment in this area.

After nearly a quarter of a century, Artificial Intelligence, in spite of all its promise, has made virtually no progress in the domain of marketing, and whilst most interested parties view it as a potentially powerful way of beating the competition, there are few products and no on‐line systems available. This article explores why progress has been slow in the domain of marketing and describes the experience and progress of a group of major British multinational companies that have joined forces to produce an Expert Marketing Planning System, EXMAR, with the author of this article as principal expert. A number of conclusions are drawn, but one of the main ones is that the development of EXMAR shows that it is possible to use Expert System methodologies to build support systems in complex areas of marketing management, especially if the domain is well defined, has a large number of factors to be considered, and relevant expert knowledge is available. Also Expert Systems are shown as being useful in helping both academics and practitioners to structure, validate and use marketing knowledge and to understand better the interrelationships between the elements of marketing. In particular, the article forces managers to think deeply and in a structured way about the issues that need to be considered in developing a strategic marketing plan.

This paper presents our experience in the development of an expert system to be used for detailed scheduling in an industrial company. This was a middle‐sized company which builds metallic components for the automotive industry. Because of the complexity of the environment where it has to work, with many interactions between related functions in the company, the development of this system has required a great deal of collaboration among different experts. So, we had to develop a more sophisticated methodology for this development. The conclusions extracted from this work lead us to a generalization of the methodology employed, which is also presented here.

Expert systems seek to solve problems by using a computer to apply reasoning methodologies to knowledge in a specific domain in order to render advice or recommendations, much like a human expert. Presents a methodology which uses the behavioral rules of multiple expert salespeople to develop a prototype of a flexible, yet systematic, sales expert system. The prototype expert system in this study was built using the selling scripts and if‐then contingency rules of expert salespeople in the insurance industry. The system mimics the selling process for interviewing, qualifying, and scheduling an appointment with an insurance prospect. While the expert system describes a relatively simple selling process, relative to the complexity of an actual face‐to‐face sales call, the procedure described is quite general.

This paper reviews the history of separate online system interfaces, leading to efforts to develop expert systems for searching databases, particularly for end users, and introduces the research in such expert systems. Appended is a bibliography of sources on interfaces and expert systems for online retrieval.

Formal planning can yield many benefits for all types of companies but many companies are experiencing difficulties with marketing planning. It has been recognized that expert systems would bring benefits to companies by helping them make better strategic plans, but the development of expert marketing systems is difficult and time consuming. Discusses some important issues related to the development of expert marketing systems. These issues are associated with understanding the domain, identifying users’ problems, knowing the limitation of expert systems and designing user‐friendly interface. These issues seem to have not been addressed adequately from expert system developers. It is hoped that discussions herein would help others to understand these issues better and to develop more successful expert marketing systems.

1. Introduction Probably the “hottest” topic in the LISA (Library and Information Systems) world in recent times is the idea of developing systems which can act “intelligently” by using the knowledge of a human expert (within our context, that of an intermediary or a librarian). Such knowledge‐based systems are popularly known as “expert systems”.

The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.

The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.

The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.

The expert system approach is not a “blind” method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.