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This paper aims to analyse the valuation of cultural products and explores what this process means for organizations involved in their production and marketing.

The authors develop the arguments using a number of mini-cases and industry examples.

The main thesis is that the meaningfulness and value ambiguity of cultural products shift the focus of valuation away from the products themselves towards how certain agents in the socio-cultural environment identify and certify these products. This paper discuss how valuation takes place via selection systems and how the nature of cultural products drives the dominance of one selection system over others.

Theories on value creation needs to take consideration of the critical role played by the selection system instead of just the firms that produce these products.

Organizations engaged in producing highly symbolic products need to manage selection systems and related industry dynamics to establish an enduring competitive advantage.

Value creation is a collective social efforts. Every member of the society can play a central role in this process. Better engaging various member of the society to enable them actively participate in the value creation process is what organizations today need to consider, instead of just treating individuals in the society as a “customer” who only passively consume. This research calls for the true empowerment of every member of the society to facilitate collective creativity and participation in the value creation endeavour that benefits the entire society as a whole.

It is the first paper that has created a conceptual link between the type of selection system and product categories. In other words, it takes existing literature on value creation and selection system one step further by creating the alignment or match between types of selection system and types of product categories. Therefore, it offers academics and practitioners a much detailed understanding on how value creation is conducted across different product categories.

Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing organization. This can be achieved through employing an appropriate material handling equipment (MHE) capable of performing the desired operation. Therefore, choosing a right MHE from the available options is a key concern for the success, growth and competitiveness of a manufacturing organization. The purpose of this paper is to describe the design and development of an expert system based on quality function deployment (QFD) methodology in Visual Basic 6.0 for selecting the most appropriate industrial truck which is a commonly practiced MHE in any manufacturing organization.

A QFD-based approach is adopted to incorporate customers’ needs into the evaluation criteria on the basis of which industrial truck selection is carried out. The applicability of the developed expert system in solving industrial truck selection problems is demonstrated using two illustrative examples.

While applying this QFD-based model, CPCD 80x manufactured by Heli is recognized as the most suitable forklift truck for transporting unitized loads within a manufacturing unit with some spatial constraints, and for loading/unloading packages/boxes/cartons and place them at the desired locations in a manufacturing unit, ETV 216 manufactured by Jungheinrich evolves out as the most suitable reach truck.

Till date, numerous research articles have been published suggesting the applications of various mathematical models, multi-criteria decision-making methods and knowledge-based systems for solving MHE selection problems, and it is intriguing to note that none of the previously adopted methods has proposed a systematic procedure for selection of the evaluation criteria and interrelated the needs of customers with the technical specifications of MHEs while identifying the best alternative for performing a specified operation. These issues can be addressed through application of this developed QFD-based expert system, which can translate customers’ needs into organizational functions that are implementable in the decision-making/selection procedure.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

A key task in the material handling system design process is the selection and configuration of equipment for material transport and storage in a facility. Material handling equipment selection is a complex, tedious task. However, there are few tools other than checklists to assist engineers in the selection of appropriate, cost‐effective material handling equipment. This paper describes the development of an intelligent material handling equipment selection system called MHESA (Material Handling Equipment Selection Advisor). The MHESA is composed of three modules: a database to store equipment types with their specifications; a knowledge‐based expert system for assisting material handling equipment selection; and an analytic hierarchy process (AHP) model to choose the most favorable equipment type. The concept proposed in this paper can automate the design of material handling equipment selection system, and provides artificial intelligence in the decision‐making process.

The purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.

The framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.

The results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.

Disruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

The workforce management model conceptualised for the effective management of the construction workforce was subjected to expert scrutiny to determine the suitability and applicability of the identified practices and their attributed variables to the construction industry. In achieving this, a Delphi approach was adopted using experts from construction organisations in South Africa. These experts comprised workforce management personnel and construction professionals in senior management positions. The data were analysed using appropriate statistical tools such as interquartile deviation, Kendell’s coefficient of concordance, and chi square to determine consensus among these experts. After a two-round Delphi, the seven constructs proposed in the conceptualised workforce management model were adjudged to be important and worthy of adoption by construction organisations seeking to improve workforce management in the current fourth industrial revolution era.

Community question answering (CQA) websites provide an open and free way to share knowledge about general topics on the internet. However, inquirers may not obtain useful answers and those who are qualified to provide answers may also miss opportunities to share their expertise without any notice. To address this problem, the purpose of this paper is to provide the means for inquirers to access archived answers and to identify effective subject matter experts for target questions.

This paper presents a question answering promoter, called QAP, for the CQA services. The proposed QAP facilitates the use of filtered archived answers regarded as explicit knowledge and recommended experts regarded as sources of implicit knowledge for the given target questions.

The experimental results indicate that QAP can leverage knowledge sharing by refining archived answers upon creditability and distributing raised questions to qualified potential experts.

This proposed method is designed for the traditional Chinese corpus.

This paper proposed an integrated framework of answer selection and expert finding uses the bottom-up multipath evaluation algorithm, an underlying voting model, the agglomerative hierarchical clustering technique and feature approaches of answer trustworthiness measuring, identification of satisfied learners and credibility of repliers. The experiments using the corpus crawled from Yahoo! Knowledge Plus under designed scenarios are conducted and results are shown in fine details.

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

The created portfolio is comparable and even profitable than several major global stock indices.

This work aims to benefit individual investors, investment managers and market onlookers.

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

This article reports on the creation of a prototype, Web‐based, expert system utility that helps end‐users better navigate the range of library databases available at the University of Illinois at Urbana‐Champaign (UIUC). Both librarian‐assigned database descriptors and terms drawn from the controlled vocabularies of the databases themselves are used to thoroughly characterize resources. End‐users then utilize keyword searches and/or menu selections to identify resources most relevant to their information needs. In addition to reporting on the UIUC prototype and the work done to create it, the concerns that gave rise to the project are discussed. Previous work and research elsewhere are summarized, and the more common approaches currently in place in academic libraries today are noted. Plans for testing the UIUC prototype with librarians and end‐users, for evaluating the results of those tests, and for iteratively refining the tool based on those evaluations are then briefly described.