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The motivation for ISO 9000 certification: a multivariate predictive approach

2022-09-26Journal article Open access
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dc.contributor.authorTexeira Fernandes Justino, Maria Do Rosário
dc.contributor.authorAlmaça, José Figueiredo
dc.contributor.authorTexeira Quirós, Joaquín
dc.contributor.authorAntunes, Marina Godinho
dc.contributor.authorMucharreira, Pedro Ribeiro
dc.date.accessioned2023-02-09T12:43:55Z
dc.date.available2023-02-09T12:43:55Z
dc.date.issued2022-09-26
dc.descriptionArtigo publicado em revista científica internacionalpt_PT
dc.description.abstractOur empirical study compares the responses provided by certified and non-certified companies using multivariate predictive models. Conceptually, there is a subtle difference between comparing questionnaire responses with T-tests and multivariate predictive models. The former should be employed when we conjecture that certification conducts to different responses to the questionnaire; the latter should be used when we believe that the respondent’s perception on the intensity with which quality practices are implemented in their firms is a predictor of certification. That is, rather than asking whether certified companies provide responses that are statistically different from those provided by non-certified companies, we try to understand which of these responses predict, or are associated to the likelihood of the firm being certified. Using a multivariate approach, we identify the set of questionnaire items that are statistically associated to this likelihood, after we control for other items. We consider two types of multivariate models. First, we implement a parametric logistic regression model. Then, because an incorrect specification of the expected value of the dependent variable in the logistic model may lead to incorrect inferences, we also implement a nonparametric decision tree model. It is shown that companies in which the respondent manifests greater concern with respect to the increased competition due to globalization, the motivation of employees, and strategic planning have higher likelihood of being certified. Using a questionnaire answered by a sample of certified companies and a control sample of companies which have not been certified, we show that T-tests and multivariate models provide different insights. According to the T-tests, most responses are significantly different whether they are provided by certified or non-certified companies. On the other hand, the logistic regression model suggests that only three questionnaire items are statistically significant in predicting if a firm is certified or not. The decision tree model indicates that a fourth item also explains the likelihood of certification.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationJustino, M. R., Almaça, J., Texeira-Quirós, J., Antunes, M. G., & Mucharreira, P. R. (2022). The Motivation for ISO 9000 Certification: A Multivariate Predictive Approach. Strategies in Accounting and Management, 3(5). 000571. ISSN: 2770-6648.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/15517
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherCrimson Publisherspt_PT
dc.relation.ispartofseries;5
dc.subjectQualitypt_PT
dc.subjectISO9000pt_PT
dc.subjectTQMpt_PT
dc.subjectLogistic regressionpt_PT
dc.subjectDecision treept_PT
dc.titleThe motivation for ISO 9000 certification: a multivariate predictive approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage7pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleStrategies in Accounting and Managementpt_PT
oaire.citation.volume3pt_PT
person.familyNameTexeira Fernandes Justino
person.familyNameTexeira Quirós
person.familyNameGodinho Antunes
person.familyNameRibeiro Mucharreira
person.givenNameMaria do Rosário
person.givenNameJoaquín
person.givenNameMarina
person.givenNamePedro
person.identifier1713364
person.identifier3507951
person.identifierUosKJfUAAAAJ&hl=pt-PT
person.identifier.ciencia-id4917-732B-5C33
person.identifier.ciencia-id601F-79BA-3BCB
person.identifier.ciencia-id7512-9AB0-FDF0
person.identifier.ciencia-id4E1D-9BEA-8DB4
person.identifier.orcid0000-0002-2120-4892
person.identifier.orcid0000-0002-7693-9888
person.identifier.orcid0000-0001-5363-9439
person.identifier.orcid0000-0003-0059-0576
person.identifier.ridGQP-6967-2022
person.identifier.scopus-author-id55856106700
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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