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The purpose of this paper is to investigate the range sensitivity of the analytic hierarchy process (AHP) and evaluate the effectiveness of using a bottom‐up approach to mitigate the possible range insensitivity bias in the AHP.

An experiment was conducted to test the normative range‐sensitivity of four different methods: the AHP with bottom‐up evaluation; direct ratio weights; swing weights; and trade‐off weights. Also, the significance of the range‐sensitivity effects and the differences among weighting approaches were rigorously tested using various statistical models.

Results show that the range sensitivities of AHP and direct ratio weights are significantly less than the range sensitivities of swing weights and tradeoff weights, suggesting that the bottom‐up evaluation approach might not be a feasible solution for the range‐insensitivity problem. This finding is consistent with the value‐comparison hypothesis proposed in an earlier study, and is partially supported by the theory of the multi‐dimensionality of attribute importance.

It is concluded that treating the attribute weights and performance scoring scales separately in the AHP or other multi‐attribute decision analysis models might lead to an arbitrary final ranking of alternatives. Therefore, it may be necessary to incorporate better elicitation procedures into the AHP models to ensure that attribute weights properly reflect the range or scale of measurement.

This study provides new evidence and issues words of warning of the range‐sensitivity effects in the multi‐attribute decision analysis.

The purpose of this paper is to demonstrate how the ICS intellectual capital methodology was developed starting from the underpinning academic theory.

The approach is founded upon a number of theoretical strands. The basic intellectual capital approach is based on a development of the resource based theory of the firm. Most intellectual capital approaches have problems with meaningful measurement. ICS addresses the valuation of intellectual capital resources by using axiology and multi‐attribute value theory to produce a valuation framework and measurement theory to ensure that the results are reliable.

The ICS intellectual capital approach generates navigators (maps) of how resources are used in companies which have proven to be very useful. It has also demonstrated the value of deeper analysis of the intellectual capital resources. The measurement part, which is often used independently (known as the Conjoint Value Hierarchy (CVH), is shown as a powerful aid to decision making as well as to more straightforward valuation.

The limitations are that the navigator and its associated analyses are non‐rigorous while the CVH is rigorous, transparent and auditable. This mismatch can lead to problem and the challenge is to integrate them.

While parts have been reported previously, this paper is the first integrated review of ICS’ methodology.

Emergency situations may differ in many ways but they share some common characteristics, such as the sudden onset and the need to transparently evaluate various usually conflicting objectives. In nuclear power generation, however, emergency situations constitute a special challenge. The focus of this paper is to highlight the role of multi‐criteria decision analysis (MCDA) in nuclear emergency and recovery management on the basis of a hypothetical case study.

Multi‐attribute value theory as one field of research within MCDA is introduced. Special emphasis is placed on the modelling of the decision makers' preferences which is a crucial part in any multi‐criteria analysis. A central aim is to facilitate the preference elicitation in group decision processes.

The management of emergency situations in nuclear power generation necessitates the consideration of technical, economic, environmental, socio‐psychological and political aspects. Furthermore, various stakeholder and expert groups with diverse background knowledge and different views, responsibilities and interests are involved in such a decision‐making process. MCDA can help to take into account various incommensurable aspects and the subjective preferences of the decision makers and thus contribute to transparency and traceability of decision‐making processes. Since the preference parameters are inherently afflicted with uncertainties, thorough sensitivity analyses are important to visualise the impact of the uncertainties in an understandable way.

A new approach to sensitivity analysis is proposed, allowing one to comprehensibly visualise and communicate the impact of the uncertainties associated with the subjective preference parameters on the results of the decision analysis.

Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

The purpose of this paper is to describe the logic and processes for identifying, measuring and managing intellectual capital resources along side traditional economic resources to achieve sustainable outcomes valued by investors.

The approach is founded on classical finance theory and draws on and multi‐attribute value theory, system dynamics and the strands of thought known as intellectual capital in order to produce a grounded framework that both produces reliable results as well as achieves acceptance in the financial community.

The findings from this approach, the FVMT Methodology, provide a comprehensive management framework that is agnostic as to the form of resources being utilized and the activities that are involved in the transformation of one resource form into another on the way to achieving sustainable outcomes valued by investors.

Whilst the approach has proven to work well the limitations are that it requires both effort and access to market makers.

The implications is that for the first time an approach is now available that provides the same rigor for managing the future value component of a firm's share price as there already exist for managing the current value component of the firm's share price. This means that managers can both reduce the volatility surrounding their share price as well as predict the value outcomes of a given set of actions.

The authors believe that this is the first presentation of a methodology grounded in classical finance theory for the managing of the future value component of a firm's share price.

Recent literature has proposed many theoretical methods to help decision makers choose an appropriate project delivery system (PDS) in a rational manner. None of these articles however systematically compare and systematize the available PDS selection methods and guide decision makers in choosing a method that best meets their PDS decision‐making circumstances. This paper aims to bridge this gap.

Four groups of PDS selection methods, namely, guidance (e.g. decision charts and guidelines), multi‐attribute analysis (e.g. multi‐attribute utility theory and analytical hierarchical process), knowledge‐ and experience‐based (e.g. case‐based reasoning), and mix‐method approaches are reviewed, compared and systematized.

The discussed methods vary in their underlying concepts, complexities of implementation and levels of required information. They also differ in the ways how decision makers' preferences are elucidated, expressed and measured. A conceptual framework is proposed to help decision makers match a PDS selection method with their decision‐making circumstances.

The paper highlights limitations of the discussed methods, and presents areas for future research.

This paper helps decision makers develop a fundamental understanding of the available PDS selection methods, and match a PDS selection method with their unique decision‐making circumstances. Using a suitable method will improve the decision‐making efficiency.

This study aims to identify the suitable indicators and their scale for measuring the level of logistic halal implementation and then develops the measurement system based on those indicators and scales. Moreover, this research also applies the measurement system in food, beverage, and ingredient companies.

This study collected data through web-based closed questionnaires and short telephone interviews. This study succeeded in obtaining seven valid data sets from filling out validation and analytical hierarchy process questionnaires by a panel of experts. This study also obtained 97 valid data sets from filling out the questionnaire about the current condition of each indicator related to halal logistics (HL) implementation. Then, the framework for measuring HL implementation was designed based on the multi-attribute value theory approach.

The aggregate value of HL implementation in ingredient companies belongs to the excellent implementation category. In contrast, the aggregate value of HL implementation in food and beverage companies only belongs to the good implementation category. Then, according to the size of the company, the large-sized companies have the higher means of the aggregate value of HL implementation for preferred indicators than small- and medium-sized companies. However, all size companies belong to the good implementation category.

This research has several limitations. The preferred indicators are only measured by the Likert scales. Future research may benefit from inducing the qualitative approaches to measure the condition of each indicator better. Secondly, this research only consisted of 97 food, beverages and ingredients companies. Thirdly, this research only measured the HL implementations to a particular sector or industry. Future research could benefit from replicating the work in similar and dissimilar contexts, adding the sample size and comparing the level of HL implementation between Muslim and non-Muslim companies, and moreover, enhance the measurement of HL implementation by including consumers and other actors involved, such as the government.

This research provided a basis for helping the government and policymakers understand and evaluate non-performing indicators in HL implementation before formulating several actions.

Understanding the current HL implementation conditions could facilitate a more effective response in designing some appropriate alternative actions.

This research contributed to measuring HL implementation with a new indicator, a new scale and a new object in an empirical case of food, beverages and ingredient companies in Indonesia.

While there is an established body of literature that discusses the importance of stakeholder management, and also the need for involvement of all stakeholders so that all values of a heritage site can be captured in a heritage management plan, the concepts are not generally developed in ways that make them useful in practice. This research seeks to bring greater clarity to the practice of stakeholder engagement in built heritage, so that organisations can manage their stakeholders in ways that meet their strategic goals. This study proposes a novel method to identify stakeholders, a stakeholder preference mapping approach, which will depict their influence on decisions based on a of power-interest scale.

This research posits a stakeholder preference mapping approach. Virtual Stakeholder Groups (VSG) were identified and stakeholder's significance impacts were measured using the RIBA Plan of Work 2013 to determine in-depth consideration of each stakeholder's power and interest against differing stages of a heritage project. Participants were convened through a 5-day workshop, consisting of 20 Malaysian and 19 international participants (80% academics and 20% Malaysian civil servants). The Multi-Attribute Decision Analysis (MADA) technique was then used to demonstrate how stakeholder identification and analysis can be used to help heritage teams meet their mandates.

The research identified eight virtual VSG (Extremist, Expert, Economic, Social, Governance and Tourists) and their scale of power-interest influence at different stages of the heritage management process. The findings reveal varying levels of engagement from each of the different groups of stakeholders at each work stage – with Stage 5 (Construction) being the least engaged.

It is anticipated that through stakeholder preference mapping, heritage teams can increase the robustness of their strategies by identifying and effectively managing the important concepts; heritage teams can effectively manage the interface between the many (often competing) demands of differing stakeholders. Using Georgetown as a case study, the research team were able to delineate the interaction and interplay between the various stakeholders in the complex decision-making processes for a UNESCO heritage site. Applying the RIBA 2013 Plan of Work as a framework to the heritage management process enables a formalised mapping approach to the process.

As the modern manufacturing twining seamlessly with logistics operations for value adding services, logistics service is becoming more and more significant. Under this research background, the purpose of this paper is to introduce an innovative evaluation model for customer satisfaction using fuzzy analytic hierarchy process (FAHP).

This model uses triangular fuzzy concept to determine the weight of each index so that subjective or objective weighting is addressed. A case study from two large express companies in China is used to demonstrate the feasibility and practicality of the proposed model for examining customer satisfaction.

One of the key findings is that Company B has higher customer satisfaction than Company A due to its quick response and flexible logistics strategy. This paper has several contributions. First, A FAHP-based customer satisfaction evaluation model is proposed for the logistics service. Second, the triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method. Third, a case study demonstrates the implementation of the model.

First, this paper considers the fuzzy AHP for the customer satisfaction evaluation. Comparing with other multi-criteria decision-making methods like data envelopment analysis, evidential reasoning approach, and multi-attribute value theory will be carried out in the near future. Second, the manufacturing modes like make-to-order, make-to-stock, and mass-customized production may have different logistics support so that the final products may reach the final targets quickly. How to evaluate various mode-based logistics and their customer satisfactions have great significance. Finally, Big Data-enabled customer satisfaction evaluation approaches may be a possible solution.

Based on the data from questionnaire, it is found that, in practical applications, manufacturing enterprises should amend the index system according to the specific business scope and the production characteristics. Manufacturing enterprises need to collect large amounts of data through market research and conduct the measurement on the related coefficient between the measurement indicators and customer satisfaction degree. After that, they can make sorting and filtering on the measurement index according to the measurement results.

Customer satisfaction is very important to manufacturing and logistics enterprises due to its time constraints. The physical products with services like logistics are paid close attention to by the final customers.

The contribution of this paper is as follows: a FAHP-based customer satisfaction evaluation model is proposed for the logistics service; triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method; a case study was used to demonstrate the implementation of the model. One of the key findings is that Company B has higher customer satisfaction than Company B due to its quick response and flexible logistics strategy.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.