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Property investment requires accurate market forecasts. The use of judgemental forecasting to predict property market performance is well established and widely practised. However, the need to deal with a large number of variables and to assess the impact of structural, exogenous and policy changes makes forecasting an inextricably difficult task, given the limitations of holistic forecasting. Proposes the incorporation of the analytical hierarchy process (AHP) to translate expert judgement into accurate and meaningful market forecasts. Such an approach facilitates communication of expert judgement and at the same time provides feedback for the expert. Provides a 12‐month forecast of the Singapore residential property market to illustrate the expert judgemental‐AHP approach.

In the face of the failure of command economic systems and the introduction of free‐market principles in Central and Eastern Europe, there will be an increasing need to provide timely and relevant information to business people who may be interested in marketing their products or services to these countries. One practical way to present this information in software form is through the use of knowledge bases and expert systems. Knowledge bases contain information which can be obtained through specific queries or via keyword searches. Expert systems act as decision‐support tools, providing consultation and advice in much the same manner that a human expert would use. Describes an expert system designed to aid the international manager in decision making, particularly with respect to target market evaluation and selection. It includes specific information on several Eastern European nations, as well as many more traditionally capitalistic countries. Descriptions of the technical structure and theoretical foundations of this expert system are presented, as well as its uses and implications for future development.

The conditions under which forecasts from expert judgement outperform traditional quantitative methods are investigated. It is shown that judgement is better than quantitative techniques at estimating the magnitude, onset, and duration of temporary change. On the other hand, quantitative methods provide superior performance during periods of no change, or constancy, in the data pattern. These propositions were tested on a sample of real business time series. To demonstrate how these propositions might be implemented, and to assess the potential value of doing so, a simple rule is tested on when to switch from quantitative to judgemental forecasts. The results show that it significantly reduces forecast error. These findings provide operations managers with some guidelines as to when (and when not) they should intervene in the forecasting process.

Theory suggest that valuers will rely on previous values estimates in the face of greater market uncertainty. Nevertheless recent research has provided evidence that experienced real estate valuers (appraisers) working in geographic areas familiar to them may not be influenced by the previous value judgement of other, anonymous experts. Presents a study which extends the previous investigation by examining appraisers valuing property in geographic areas unfamiliar to them, appraisers who therefore face significant market uncertainty. In controlled experiments valuers were asked to appraise a subject property in an area unfamiliar to them and were offered the previous value judgement of an anonymous expert as a potential anchor (reference point). A control group of experts unfamiliar with the subject market was given no reference point whatsoever. Evaluation of the experimental data revealed evidence that unlike subjects operating in areas of familiarity, subjects unfamiliar with the subject area were influenced by the provided reference point.

Discusses the quality of fabrics and garment technology. Looks at the various types of material focusing particularly on the female market. Shows how new synthetic fibres have improved the feel of garments and that consumers, fashion designers and experts in finishing have all reached the same conclusion, that quality judgement is still possible, even with today's exacting standards.

The purpose of this paper is to identify the core dimensions of problem solving of experts in commercial valuation in order to provide a rich stimulus for managing current practice and enabling future development.

The study adopted a cognitive position but emphasised understanding the everyday commercial property valuation practice in a naturalistic context and from the participants’ perspectives. Given this, a grounded theory approach was employed as a research strategy to guide the data collection and surface theoretical interpretations. Data were obtained through in-depth interviews with practicing valuers working in private real estate firms within metropolitan Birmingham, UK.

The interviews uncover four dimensions of experts’ problem-solving practice in commercial valuation: multidimensional, domain-specific knowledge base; cognitive process that is centred on analysis and reflection; collaborative problem-solving venture with colleagues; and professional practice issues awareness. A conceptual model is proposed which integrates these dimensions enabling a clearer understanding of the nature of valuation work.

This study was designed to be descriptive and theory generating, thus, the findings cannot be generalised as the sample was confined to one city and consists of a small number of senior practicing valuers. Therefore, the findings may not be fully applicable to other practicing valuers, other geographical locations or more widely to other types of property valuation. Nevertheless, the findings provide an important cognitive framework which can be verified by other researchers seeking to examine the practice of expert valuers.

The identification of the core dimensions of expert problem solving in commercial property valuation is shown to have implications for valuation practice, education and continued research. The valuation practice environments need to develop mechanisms to provide time that would enable these multi-dimensions of professional competence to be developed. Further work is needed to expand and refine the model across expert practice in other specialty areas of valuation practice.

This study expands the current understanding of valuation process to areas of expertise that have received less coverage in behavioural valuation literature, that is, the central role of knowledge and cognition and how these are integrated for effective valuation problem solving and decision making.

Reportedly, green roof (GR) makes a significant contribution towards a truly sustainable-built environment; however, its implementation is yet to hit a sufficient level in developing countries. Thus, this study assesses GR implementation strategies in developing countries by providing a comparative analysis through experts in Kazakhstan, Malaysia and Turkey.

The study adopts a four-step methodological approach to achieve the research aim: literature review, focus group discussion, fuzzy analytical hierarchy process (FAHP) analysis and correlation analyses. First, a literature review followed by a focus group discussion is used to determine 18 (out of 25 initially) strategies for the selected context and these are classified into three categories: governmental and institutional support, knowledge and information and policy and regulation. Afterward, the identified GR strategies are evaluated using the FAHP with the data gathered from the experts in the countries studied. Finally, correlation analyses were used to observe the strength of agreement between the assessments of experts from the included countries.

The findings indicate that financial incentives, low-cost government loans and subsidies and tax rebates are the essential strategies for the wider adoption of GR. Evaluating the policy and regulations strategies also showed that mandatory GR policies and regulations and better enforcement of the developed GR policies are ranked as the most prominent strategies. The findings show a low level of agreement among respondents from Kazakhstan, while there is a high level of agreement between the experts in Malaysia and Turkey.

The research contribution is twofold. First (research implication), the study identifies the strategies through a complete literature review. Second, the identified strategies are evaluated through the lenses of experts in three developing countries which are hoped to provide (practical contribution) a better understanding of the most effective strategies that require attention and enable the frontline stakeholders (particularly government authorities) to focus on them.

The study findings provide a good point of departure to explore the strategies for broader adoption of GRs in developing economic setting.

The purpose of this paper is to explore and analyse the critical success factors (CSFs) of humanitarian supply chains in mitigating the impact of cyclones in the Indian context using the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach.

The selection of CSFs of humanitarian supply chains has been done through several secondary sources and discussion with 12 disaster experts. Thereafter, DEMATEL, an expert judgement-based technique, has been used for selecting, building and analysing a structural model that involves causal relationships between the set of identified CSFs. Furthermore, to accommodate the vagueness involved in human judgement, fuzzy logic is incorporated with the DEMATEL.

Based on a literature survey and expert judgement, total 16 CSFs of humanitarian supply chains have been segmented into cause and effect groups based upon their relative influencing scores. The analysis shows that the out of 16 CSFs, ten CSFs have been categorised into cause group CSFs and six as the effect group CSFs.

The findings of this study will help disaster management institutions, humanitarian agencies, logisticians, NGOs and cyclone-prone countries to improve the critical ingredients in designing and executing an effective cyclone response operation.

In this study, fuzzy DEMATEL has been applied to identify and analyse the CSFs of humanitarian supply chains for the cyclone disaster response in the Indian context, which is a novel contribution widening the existing knowledge in humanitarian relief domain.

The purpose of this paper is to examine critically the accuracy of expert judgment, drawing on empirical evidence and theory from multiple disciplines. It suggests that counsel offered with confidence by experts might, under certain circumstances, be without merit, and presents approaches to assessing the accuracy of such counsel.

The paper synthesizes research findings on expert judgment drawn from multiple fields, including psychology, criminal justice, political science, and decision analysis. It examines internal and external factors affecting the veracity of what experts may judge to be matters of common sense, using a semiotic structure.

In multiple domains, including management, expert accuracy is, in general, no better than chance. Increased experience, however, is often accompanied by an unjustified increase in self‐confidence.

While the dynamic nature of decision making in organizations renders the development of a codified, reliable knowledge base potentially unachievable, there is value in recognizing these limitations, and employing tactics to explore more thoroughly both problem and solutions spaces

The paper's originality lies in its integration of recent, multiple‐disciplinary research as a basis for persuading decision makers of the perils of accepting expert advice without skepticism.

Proposes a possibilistic group support system (PGSS) for the retailer pricing and inventory problem when possibilistic fluctuations of product parameters are controlled by a set of possibilistic optimality conditions. Experts in various functional areas convey their subjective judgement to the PGSS in the form of analytical models (for product parameters estimation), fuzzy concepts (facts), and possibilistic propositions (for validation and choice procedures). Basic probability assignments are used to elicit experts’ opinions. They are then transformed into compatibility functions for fuzzy concepts using the falling shadow technique. Evidence is processed in the form of fuzzy concepts, then is rewritten back to basic probability assignments using the principle of least ignorance on randomness. The PGSS allows the user (inventory control) to examine a trade‐off between the belief value of a greater profit and a lower amount of randomness associated with it.