An empirical comparison of three variants of the AHP and two variants of conjoint analysis

Mulye, Rajendra

doi:10.1002/(sici)1099-0771(1998120)11:4<263::aid-bdm301>3.0.co;2-t

Cited by 39 publications

(53 citation statements)

References 17 publications

(41 reference statements)

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“…Other methods that have attempted to elicit and quantify user needs in healthcare as the conjoint analysis (CA) , discrete choice experiments (de BekkerGrob, et al, 2012) and best-worst scaling (Gallego et al, 2011). Compared to these other methods, the AHP seems to be more flexible (Ijzerman et al, 2008) and robust (Scholl et al, 2005), especially when an assessment involves many attributes (Mulye, 1998). However, other studies highlighted some limits of the AHP when proper tools are not utilized (Ijzerman, et al, 2012).…”

Section: The Analytic Hierarchy Process Methodsmentioning

confidence: 99%

Government policy and healthcare management: proposal of a shared decision-making model

Petrillo

Felice

Vanzanella

et al. 2015

IJMDM

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(2015) Government policy and healthcare management: proposal of a shared decisionmaking model. International Journal of Management and Decision Making, 14 (3). pp. 183-204. ISSN 1741-5187 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/31734/1/Petrillo%202015%20IJMDM%20Government %20Policy%20and%20Health%20Care%20management-%20proposal%20of%20a %20shared%20decision-making%20model%20-%20final%20post-review%20author %20version%20with%20h.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Vol. 14, No. 3. pp.183 -204. doi: 10.1504/IJMDM.2015 Fabio De Felice, Professor at the University of Cassino and Southern Lazio, board member of several international organizations and responsible for scientific research and training in industrial plants. The scientific activity developed through studies and researches on problems concerning industrial plant engineering such as improvement of quality in productive processes, simulation of industrial plants, multicriteria techniques, RAMS Analysis and Human Reliability Analysis. General Secretary of the Analytic Hierarchy Process -AHP Academy -International Association for the promotion of multi-criteria decision making methods.Carmela Vanzanella, Researcher at Italian Council of National Researches in Rome (Italy), the largest public research institution in Italy. She conducts research activities on Medical Technology, Neuroradiolgy, Health technology assessment in order to provide technologies and solutions to emerging public and private needs, to advice Government and other public bodies, and to contribute to the qualification of human resources.Filippo Crispino, works as an entrepreneur and engineer in information technology for over 10 years. He has been CTO and CEO of several companies. In the last three years has been delegated to the "Research, Innovation and Technology Transfer" and chairman of the Young Entrepreneurs of Confindustria Campania, he has been researcher in the field of Operations Research at the Faculty of Engineering at the University of Sannio, has participated in several research projects with universities and government agencies, has designed information systems and developed software for the Province of Rome, Avellino, University Nottingham and several companies. Stephen Morgan, is Professor of Biomedical Engineering at the University of Nottingham....

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Section: The Analytic Hierarchy Process Methodsmentioning

confidence: 99%

Government policy and healthcare management: proposal of a shared decision-making model

Petrillo

Felice

Vanzanella

et al. 2015

IJMDM

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“…Concerning AHP we use the original version described by Saaty (1980) based on an incomplete hierarchy, because several empirical and experimental studies comparing different variations of the method indicate that neither dominates the other (cf. Mulye, 1998;Zanakis et al, 1998). In case of CA, we use a paired-comparison approach in order to get evaluation tasks comparable to those of AHP.…”

Section: Description Of the Methodsmentioning

confidence: 99%

“…In an incomplete hierarchy only the different levels of the attributes are integrated in the hierarchy and connected to the respective attribute. The latter approach is called absolute measurement (Saaty, 1987, p. 42;Tscheulin, 1992, p. 27;Mulye, 1998).…”

Section: Hierarchy Of Attributes and Alternativesmentioning

confidence: 99%

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Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: An empirical comparison

Schöll

Manthey

Helm

et al. 2005

European Journal of Operational Research

126

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“…AHP is an analytical method that was introduced by Saaty (1977;1980) and is regarded as a useful multi-attribute decision making tool in determining relative importance of certain variables (Kumar et al, 2015;Mulye, 1998). AHP is a method for relative measurement, useful in situations when there is a need for making a choice among a set of alternatives based on defined criteria.…”

Section: Fuzzy Analytic Hierarchy Processmentioning

confidence: 99%

Benefit Segmentation of Outbound Summer Package Tourists

Zečević

Kovačević

2017

FU Econ Org

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Abstract. This study assesses tourist preferences for summer package vacation benefits. The purpose of this paper is to classify summer package tourists based on preferred benefits they seek from their vacation. Analysis of preferences for benefits sought is done by using the Analytic Hierarchy Process (AHP). A sample of 850 respondents was interviewed by telephone in order to reveal their preferences. In order to reveal the grouping of tourists according to their preferred benefits, a two-step cluster analysis has been applied using the log-likelihood measure, while the number of clusters has been determined using Schwarz's Bayesian Criterion. Four segments have been identified based on the benefits sought: variety seekers, package-centrics, comfortseekers and safety seekers. This research showed that fuzzy AHP could be used not just for understanding of tourist preferences, but also as a base for segmentation. This is a first attempt to apply fuzzy AHP for that purpose in tourism.

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An empirical comparison of three variants of the AHP and two variants of conjoint analysis

Cited by 39 publications

References 17 publications

Government policy and healthcare management: proposal of a shared decision-making model

Government policy and healthcare management: proposal of a shared decision-making model

Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: An empirical comparison

Benefit Segmentation of Outbound Summer Package Tourists

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