Search results

The purpose of this study is to investigate consumer reactions to professionals who use decision aids to make recommendations. The authors propose that people react negatively to decision aids only when they are used in place of human expert judgment. When used in combination with expert judgment, decision aids are not perceived negatively and may even enhance service evaluations.

Three online experiments are presented. Participants indicated their perceptions regarding the recommendation strategy of professionals and their impressions of these professionals using one of three strategies: one based on expertise only, one based on decision aids only and a combination of the two (hybrid approach). Both within and between-subjects designs were used.

Contrary to previous research that has found a negative reaction to professionals who use decision aids, the authors find that consumers actually appreciate these professionals, as long as the use of decision aids does not replace expert judgment. The authors also find that when people are given the opportunity to compare a pure expert judgment approach with a hybrid approach (decision aid in combination with expert judgment), they prefer the latter.

Although findings should extend to various contexts, this research is limited to the three contexts examined and to the type of use of decision aid described.

It has significant practical implication, as decision aids have been shown to improve decision accuracy, but previous research had indicated that consumers view these professionals in a negative way. The current research more clearly delineates the situations under which negative reactions are likely to occur and makes recommendations regarding circumstances in which reactions are actually quite positive.

Reactions to professionals using decision aids have been investigated outside the marketing literature. However, this is the first work to show that consumers actually have positive reactions to professionals using decision aids, as long as they do not replace expert judgment.

India's textile industries play a vital role in the Indian economy. These industries consume the highest thermal energy (steam power). The demand of the steam in process industries is increasing rapidly, and this demand can be met by increasing the capacity utilization of steam boilers. The purpose of this paper is to present a new approach for reliability analysis by expert judgment method.

A lack of adequate life data is one of the biggest challenge in the reliability analysis of mechanical systems. This research provides an expert judgment approach for assessing the boiler's reliability characteristics. For this purpose, opinions of experts on time to failure and time to repair data were elicited in the form of statistical distributions. In this work, reliability analysis of the boiler system is carried out by expert judgment method and by using best-fit failure model. The system reliability along with preventive maintenance intervals of all components is also evaluated.

It is observed that the reliability analysis results obtained by expert judgment method and best-fit failure model method indicate that there are no significant differences. Therefore, in case when insufficient data are available, the expert judgment method can be effectively used. The analysis shows that the feedwater tank, feedwater pump, supply water temperature sensor, strainer, return water temperature sensor, condensate filter, mechanical dust collector, coal crusher and fusible plug are identified as critical components from a reliability perspective, and preventive maintenance strategy is suggested for these components.

In this research paper, a system reliability model by the expert judgment method is developed, and it can be effectively used where insufficient failure data are available. This paper is useful for the comparative evaluation of reliability characteristics of a boiler system by expert judgment method and best-fit failure model method.

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.

The paper aims to introduce a model for measuring individual performance named Workforce Competency Model (WFCM). Notable aspects of the model including its validation/verification are explored and presented by means of a case study.

The model is developed by referring to the previously developed models in literature, which are concerned with the measurement of human or individual performance. Taking into account the organization's needs for each of the workers to be evaluated on identified indicators, performances of individual maintenance workers are quantified according to factors that contribute to those indicators.

Previous studies show the significance of human involvement in maintenance has been highlighted in many industries; the human element is one of the primary aspects that influence maintenance effectiveness. A model for quantifying individual workers' performance is introduced. A case study carried out in an electronic packaging company for verification and validation purpose demonstrates the developed model is efficient, accurate, flexible and feasible.

This paper presents a unique approach to developing a model that can be used as a tool to effectively evaluate maintenance workforce performance. This study involves a thorough review of the past literature to design the concept to be used, the development process of the model, and validation of the developed model through a case study. The great challenge to the success of this project is to develop the model so that a robust and feasible measurement tool can be built.

Housing infrastructure is the basic need for people of a community and due to disaster many houses may severaly damaged. Stakeholders and decision makers should focus on this issue and make the infrastructure more resilient against natural hazards. As dependency plays a very important role in resilience, it is important to study the dependencies and correlations among the housing infrastructure resilience factors. The evaluation of dependencies involve vagueness due to subjective judgement of experts.

In this work, the interaction between the housing infrastructure resilience factors are evaluated by using two different approaches such as crisp DEMATEL (Decision-Making and Trial Evaluation Laboratory) and rough DEMATEL (intregated crisp DEMATEL and rough set theory), where rough theory addressed the involvement of vagueness. These two approaches are compared with each other to find the effectiveness of rough DEMATEL over crisp DEMATEL.

The important factors of housing infrastructure resilience are identified by using both the approaches against flood hazard.

The limitation of rough DEMATEL method is that it does not differentiate the type of influence such as positive or negative.

The outcome of the work will helps the stakeholders and ecission makers to make the infrastructure more resilient.

This study identify the imporatnat resilience factors of housing infrastructure against flood hazard by using two methodologies.

This paper includes three analyses concerning: expert support in the selection of impact variables for scientific models relevant to environmental planning, the quality of students’ individual estimates of corresponding impacts before and after a group discussion, and the accuracy of artificially‐aggregated judgments of independent groups. Participants were students of environmental sciences at ETH Zurich. The first analysis revealed that during participation in an environmental case study, students’ individual estimates of impacts of variables which have been suggested by experts increased, as compared to the estimates of impacts of additional variables, which have been selected by the students. The remaining analyses consider group discussions on the strength of particular environmental impacts. The quality of the estimates was analyzed referring to expert estimates of the impacts.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

The purpose of this paper is to address the limitations of conventional risk matrix based tools such that both positive and negative connotation of uncertainty could be captured within a unified framework that is capable of modeling the direction and strength of causal relationships across uncertainties and prioritizing project uncertainties as both threats and opportunities.

Theoretically grounded in the frameworks of Bayesian belief networks (BBNs) and interpretive structural modeling (ISM), this paper develops a structured process for assessing uncertainties in projects. The proposed process is demonstrated by a real application in the construction industry.

Project uncertainties must be prioritized on the basis of their network-wide propagation impact within a network setting of interacting threats and opportunities. Prioritization schemes neglecting interdependencies across project uncertainties might result in selecting sub-optimal strategies. Selection of strategies should focus on both identifying common cause uncertainty triggers and establishing the strength of interdependency between interconnected uncertainties.

This paper introduces a novel approach that integrates both facets of project uncertainties within a project uncertainty network so that decision makers can prioritize uncertainty factors considering the trade-off between threats and opportunities as well as their interactions. The ISM based development of the network structure helps in identifying common cause uncertainty triggers whereas the modeling of a BBN makes it possible to visualize the propagation impact of uncertainties within a network setting. Further, the proposed approach utilizes risk matrix data for project managers to be able to adopt this approach in practice. The proposed process can be used by practitioners while developing uncertainty management strategies, preparing risk management plans and formulating their contract strategy.

This study aims to focus on the sustainability of prefabricated medical emergency buildings (PMEBs) renovation after the epidemic, to address the problem that large numbers of PMEBs may be abandoned for losing their original architectural functions. This study develops an evaluation system to identify and measure sustainable factors for PMEBs’ renovation schemes. Qualitative and quantitative analysis of PMEBs’ renovation scheme was conducted based on cloud model evaluation method and selected the renovation scheme in line with sustainable development. The study promotes evaluation methods and decision-making basis for the renovation design of global PMEBs and realizes the use-value of building functions again.

By referring to the existing literature, design standards and expert visiting a set of evaluation index systems which combines the renovation of the PMEBs and the sustainability concept has been established, which calculates the balanced optimal comprehensive weight of each indicator utilizing combination weighting method, and quantifies the qualitative language of different PMEBs’ renovation schemes by experts through characteristics of the cloud model. This paper takes Huoshenshan hospital a representative PMEB during the epidemic period as an example, to verify the feasibility of the cloud model evaluation method.

The research results of this paper are that in the PMEBs’ renovation scheme structural reformative (T11) and corresponding nature with the original building (T13) have the most important influence; the continuity of architectural cultural value (T22) and regional development coherence (T23) are the key factors affecting the social dimension; the profitability of renovated buildings (T34) is the key factor affecting the economic dimension; the environmental impact (T41), resource utilization (T42) and ecological technology (T43) are the key factors in the environmental dimension.

This study contributes to the existing body of knowledge by supplementing a set of scientific evaluation methods to make up for the sustainability measurement of PMEBs’ renovation scheme. The main objective was to make renovated PMEBs meet the needs of urban sustainable development, retain the original cultural value of the buildings, meanwhile enhance their social and economic value and realize the renovation with the least impact on the environment.

Aim of this paper is to evaluate the reliability of UNESCO Periodic Reports for the assessment of hazards affecting the UNESCO world heritage sites (WHSs) and to rank the most critical WHSs in Europe through multicriteria analysis.

The Periodic Reports represent the available continental-scale knowledge on hazards that threaten the WHSs in Europe and include 13 different natural threats. The information included in these reports has been first validated with high-quality data available in Italy for volcanoes, landslides, and earthquakes. Starting from the Periodic Reports, a multicriteria hazard analysis has been developed by using the analytical hierarchy procedure (AHP) approach. This analysis allows to identify and to rank the most critical WHSs at the European scale.

The data provided by Periodic Reports are demonstrated to be a good starting point for a continental-scale analysis of the actual distribution of natural threats affecting WHSs in Europe. The Periodic Reports appear to be reliable enough for a first-order assessment of hazards. The general overview of the hazard at the European scale shows high value of hazard index in the Eastern Mediterranean area and Balkans, due to a combination of earthquakes and landslides. The most at danger cultural site is in Bosnia and Herzegovina, while the most at danger natural site is Norway.

The paper gives a contribution to improve the continental-scale knowledge on hazards affecting the UNESCO heritage sites. The assessment of hazard inside the WHSs is an important task for the preservation of cultural and natural heritage, and it is important for UNESCO to achieve some of its goals. Through this research, European WHSs have been ranked according to their degree of hazard.