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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.

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.

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.

To outsource part of their work, organizations are looking for suppliers who also have green criteria with other criteria. Selecting suppliers begins with the definition of potential suppliers and then selects the best among them. This study aims to present a two-part approach for selecting suppliers consisting of suppliers’ prioritization.

In the first part, the criteria that influence on selecting the suppliers have been identified and extracted using the literature review and experts’ opinion which consists of 19 criteria. Then, these criteria were evaluated by the content validity ratio index and using experts’ opinions, and finally, 16 criteria were selected for selecting green suppliers in a polyethylene’s products producer company in Iran. In the next step, suppliers are selected in a green supply chain using multi-criteria decision-making methods such as Dempster–Shafer theory and grey relationship analysis, which is a strategic decision.

This study attempts to improve the level of reliance on the whole uncertain degree by combining Dempster–Shafer theory and grey relational analysis (GRA), which makes the grey analysis method more robust and its results more reliable. The findings show that Supplier 4 is ranked as first within six suppliers.

Using GRA and Dempster–Shafer theory for green supplier selection problem in polyethylene industry is the novelty of this work.

This paper proposes a multi-layer hybrid decision-making approach to evaluate the capability alternatives for developing a collaborative network to operate in the international market.

The present study is contextualised in the Iranian pistachio export industry. An extensive review of the state-of-the-art literature on supplier collaboration was conducted to identify key capabilities that are essential to establish a collaborative network. The set of defined capabilities were then optimised through interviews with 14 experts from the relevant industry, academics and export authorities. A combination of the fuzzy Delphi method and the best–worst method (BWM) approach was, respectively, used to reduce the number of capability alternatives and assign priority weights to these alternatives. Subsequently, a weighted aggregated sum product assessment method (WASPAS) was employed to rank and evaluate the ability to creating a collaborative network for the export of pistachio.

From the extant literature review, 18 capabilities for the formation of coordination networks in the international markets were identified. Then, the prominent indicators in forming a global network were extracted. After ranking the top pistachio export countries/regions to formalise an efficient collaborative network, it was revealed that although Iran exports approximately 30% of the global market, it falls behind the USA and European Union. The competitors have scored higher in critical criteria, including “trust and commitment”, “strategy and management”, “managerial control and standardization” and “financial resources”.

The proposed hybrid approach encompassing fuzzy Delphi–BWM–WASPAS offers to solve the capability evaluation and selection as well as ranking the possible alternative to formalise a collaborative network in an integrated fashion. This combination of methods is capable to first identify the most important factors, then measuring their importance and eventually rank the possible alternatives. The suggested framework provides an approach to deal with the uncertainty of global collaborative network formation.

The construction organisations in developing countries are lagging in digital technology usage; hence, they are still characterised by poor project delivery and technological backwardness. To checkmate this problem, partnering with other organisations was proposed, while critical factors needed for successful digital partnering of construction organisations were assessed.

The study adopted a qualitative research approach. Delphi method was used to get experts' opinion on some identified factors necessary for successful digital partnering. The experts for the study were selected using defined criteria, and a combination of interquartile deviation, Kendall's coefficient of concordance and chi-square was used to achieve robust consensus among respondents. Mann–Whitney U test was also used to ascertain the difference in perception of experts.

The findings revealed trust in digital partners, top management support and digital partner selection as the three key success factors for partnering. Other factors that can be considered alongside these main factors are the creation of a common goal, a long-term commitment, effective communication, proper conflict resolution and structured as well as continuous digital training, workshops and meetings.

Instead of going through the digitalisation journey alone, construction organisations, particularly in a developing country like South Africa, stand a better chance of surviving the dynamic construction and digital environment by partnering with other organisations within and outside the industry. Carefully selecting the right partner, trusting in their capability and ensuring constant commitment is necessary for the success of the process.

The value of this study lies in its ability to showcase the critical success factors needed for effective digital partnering, an aspect that lacks adequate consideration in the digitalisation of construction industry discourse.

The selection of experts for Delphi studies is crucial for the quality of the forecast results and the information taken into account. In the past, this has usually been done by selecting participants according to their reputation, although this approach is questionable in terms of reaching the most knowledgeable participants having new, relevant and valid information. In this context, this paper aims to propose to operate a prediction market alongside Delphi studies and select participants based on their trading behaviour in the market for the Delphi study.

Based on more than three years of historical prediction market trading data, the authors verify attributes that indicate insightful trades, as previously discussed in the finance literature, by using regression and classification trees.

The paper contributes attributes of trading behaviour that are theoretically derived from literature and potentially related to informed traders. These are tested and evaluated on historical prediction market data. Especially, the trading volume, the spread at the moment of trading and the market maker attribute seem to predict informed traders the best.

Algorithms based on identified attributes can be used to objectify the selection of experts for Delphi studies with potential gains in terms of the amount of information considered.