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Shows how to introduce computer support for the implementation of different decision‐making settings. Provides a comparison between decision support and expert systems. Analyses the similarities and dissimilarities of decision support systems (DSS) and expert systems (ES) and presents a conceptual framework for the integration of these two technologies. Numerous examples illustrate operationally integrated DSS and ES.

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.

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.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.

The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.

The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.

This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.

Strategic marketing planning is a complex task which involves many expert judgements. Expert systems would have a tremendous impact in this field, but the development of marketing expert systems has proven to be difficult. Describes a hybrid expert system, HYMS, for strategic marketing planning. HYMS aims to provide a structured marketing planning process, guide a user through this process, offer expert advice at key stages and finally make recommendations for users in setting objectives and strategies. It combines the advantages of the expert system and decision support system technology in order to enhance its effectiveness. Presents a a brief review on expert systems and decision support systems in marketing and then discusses the selection of a strategic marketing planning model and software for HYMS. Describes the architecture of the prototype system and the knowledge elicitation issues. Finally, proposes the further extension and enhancement of the HYMS system and offers a brief conclusion from current research.

The purpose of this paper is to introduce and identify the basic components, tasks and application areas of expert systems (ESs) as a decision support system that has been increasingly used in the business world lately and explore its potential for improving the effectiveness of administrative decisions in the public sector. Empirically, the paper explains the role of ESs in fostering decision-making processes at the Ministry of Investment and International Cooperation (MIIC) in Egypt.

The design of this research is descriptive in the theoretical section and quantitative in the empirical one. Theoretically, the study adopted both the analytical approach and systems approach to demonstrate main concepts and relationships, while it conducted an empirical study to investigate the correlations in practice.

The research concluded that the usage of ESs is deemed to be on the top of the technical solutions that might help public organizations develop their management quality and maintain competitive strength. In addition, the results proved that ESs contribute to administrative decisions at MIIC.

The paper provides profitable findings and recommendations which can be applied by Egyptian public executives, in an attempt to ensure high quality and successful decisions using modern technology.

This study has valuable implications for theory and practice together, as it offers numerous contributions to literature in the area of concern.

Discusses artificial intelligence, particularly expert or knowledge systems, and its relevance for business‐to‐business marketing and marketing decision support systems. Enables the reader to understand the basic concepts of expert systems while providing more technical references, before discussing current and potential applications for decision support systems. Argues that expert systems will ultimately play a major role in such applications, developing over the next two decades. Includes appendices and a glossary of terms and definitions.

Expert systems are computer software packages that experts in specific fields enrich with their knowledge by distilling their expertise into a set of laws for the system. The development of expert systems and the contribution they can provide in banks, whereby financial experts can produce application programmes to help lesser experts solve problems in specialised fields by responding to program queries, eg. with regard to loan approval, cross‐selling, risk analysis, treasury operations and so on, are discussed.

Past research suggests that decision-making can be improved by gauging diverse view points from key stakeholders, where a stakeholder is any group or individual that can affect or is affected by the decisions. Two key stakeholders in public policy making are economic experts that can predict the economic consequences of policy decisions, and elected policy-makers who are accountable to the citizens for the outcomes of these decisions. A two-step model combining a Decision Support System and a Delphi type approach is proposed. To test the methodology, the proposed approach was applied to revenue planning in a mid-sized city.