Study on disaster reconstruction project performance evaluation based on fuzzy analytic network process

Purpose This study aims to establish a rigorous model that can pragmatically evaluate the facilities management (FM) performance of hospitals. Design/methodology/approach Among the applicable performance indicators that were identified from extant literature, a focus group study shortlisted ten key performance indicators (KPIs) in four categories (safety, physical, financial and environmental) and verified their practicality. Using the analytic network process (ANP) method to process the focus group’s responses yielded importance weightings for the KPIs and developed the intended evaluation model. This model was then validated by a case study. Findings From the empirical data collected, two types of FM performance data and two scenarios of KPI scores were identified. To process these data and scores, a robust calculation method was devised and then proved useful in obtaining an overall score for holistic hospital FM performance. The case study confirmed the appropriateness and validity of the model developed. Research limitations/implications Through illustrating how the ANP method could be applied to develop an FM performance evaluation model, the study contributes knowledge to the multi-criteria decision-making domain. Despite the geographical limitation of the model established (i.e. centered around a group of hospitals investigated in Hong Kong), the study can serve as a reference for developing performance evaluation models for other buildings or infrastructures globally. Practical implications The model constitutes a practical tool for evaluating the FM performance of hospitals. Using this model on a regular basis will enable performance benchmarking and hence, continuous improvement of FM services. Originality/value The ANP model established is the first of its kind tailored for evaluation of hospital FM performance.

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Developing and evaluating a proposed health security framework in IoT using fuzzy analytic network process method

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02472-3 ◽

2020 ◽

Author(s):

Mohammad Bagher Haghparast ◽

Sasan Berehlia ◽

Mona Akbari ◽

Ali Sayadi

Keyword(s):

Analytic Network Process ◽

Security Framework ◽

Health Security ◽

Network Process ◽

Fuzzy Analytic Network Process ◽

Process Method

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Measuring the complexity of mega construction projects in China—A fuzzy analytic network process analysis

International Journal of Project Management ◽

10.1016/j.ijproman.2014.07.009 ◽

2015 ◽

Vol 33 (3) ◽

pp. 549-563 ◽

Cited By ~ 88

Author(s):

Qinghua He ◽

Lan Luo ◽

Yi Hu ◽

Albert P.C. Chan

Keyword(s):

Construction Projects ◽

Process Analysis ◽

Analytic Network Process ◽

Network Process ◽

Fuzzy Analytic Network Process

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Proposing a Quantitative Model Towards Building Trust in B2C E-Commerce

International Journal of Customer Relationship Marketing and Management ◽

10.4018/ijcrmm.2018010103 ◽

2018 ◽

Vol 9 (1) ◽

pp. 36-53 ◽

Cited By ~ 1

Author(s):

Mahmoud Zamani ◽

Changiz Valmohammadi

Keyword(s):

Evaluation Criteria ◽

Analytic Network Process ◽

Quantitative Model ◽

Important Indicator ◽

Building Trust ◽

Proposed Model ◽

Network Process ◽

Interdependent Relationships ◽

Novel Model ◽

Fuzzy Analytic Network Process

This article aims to develop a novel model for ranking the parameters that influence the building of trust on online shopping in the context of Iran. A multi-criteria decision making (MCDM) model based on a fuzzy analytic network process (ANP) method is developed to systematically clarify the interdependent relationships among the evaluation criteria of trust and then, scientifically evaluate the feasible parameters and rank the priorities of the trust. The results of the proposed model reveal that under the characteristics of trustor (TR) sub-factors, the most important indicator which can affect the trust is propensity to trust. Furthermore, regarding the characteristics of trustee (TE) sub-factors, data quality with a priority of 0.064 is the most important indicator. Also, in the environmental factors (EN) sub-factors, promotions and advertising with a weight of 0.151 is the most significant indicator.

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