Search results for " Conjoint analysis"

showing 2 items of 2 documents

Project Management Information Systems (PMISs): A Statistical-Based Analysis for the Evaluation of Software Packages Features

2021

Project Managers (PMs) working in competitive markets are finding Project Management Information Systems (PMISs) useful for planning, organizing and controlling projects of varying complexity. A wide variety of PMIS software is available, suitable for projects differing in scope and user needs. This paper identifies the most useful features found in PMISs. An extensive literature review and analysis of commercial software is made to identify the main features of PMISs. Afterwards, the list is reduced by a panel of project management experts, and a statistical analysis is performed on data acquired by means of two different surveys. The relative importance of listed features is properly comp…

Clustering; Conjoint analysis; Design of Experiment (DoE); Project Management Information System (PMIS); Ranking method; Surveyranking methodTechnologyComputer scienceQH301-705.5QC1-999SoftwareSettore ING-IND/17 - Impianti Industriali MeccaniciGeneral Materials SciencesurveyProject managementBiology (General)Cluster analysisInstrumentationQD1-999Fluid Flow and Transfer ProcessesCommercial softwareScope (project management)business.industryProcess Chemistry and TechnologyTPhysicsGeneral EngineeringProject Management Information System (PMIS); survey; Design of Experiment (DoE); conjoint analysis; ranking method; clusteringClustering Conjoint analysis Design of Experiment (DoE) Project Management Information System (PMIS) Ranking method SurveyProject Management Information System (PMIS)Engineering (General). Civil engineering (General)Data scienceDesign of Experiment (DoE)Computer Science ApplicationsConjoint analysisVariety (cybernetics)ChemistryRespondentconjoint analysisTA1-2040businessclusteringApplied Sciences; Volume 11; Issue 23; Pages: 11233
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Design and analysis of discrete choice experiments for models with response time

2013

Settore SECS-S/02 - Statistica Per La Ricerca Sperimentale E TecnologicaA sector of conjoint analysis (experimental design in marketing research) is made of the so called choice experiments. In choice experiments respondents undergo a questionnaire which is nowadays mostly submitted through the internet. The questionnaire proposes to the respondent a sequence of choice sets each one including two or more profiles being a profile a specific combination of attribute levels. The respondent selects the preferred profile for each choice set. Responses given by a sample of respondents are analysed through suitable methods aimed to eventually find the best combination of attribute levels. One method of analysis adopts the Multinomial Logit (MLN) model. In this article the authors show the results of the MLN analysis compared with another model of analysis which uses an additional response which can be easily recorded by electronically submitted questionnaires. In practice modern survey platforms like “Qualtrics” (the one used for this work) can record the so called “response latency” i.e. the time taken by the respondent to make the choice and select the most preferred profile in the choice set. Thanks to a response latency model further refined in this work it is possible to deduce the relative weight of importance of the profiles for each choice set and respondent. This type of response can be used in place of the dichotomous choice variable in the MLN model. The two models and methods of analysis are deeply compared and it is critically discussed when it is better to use one or the other method. As a result a more reliable estimate of the optimal profile comes up implying lower risks for new investments and marketing decisions.
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