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Collaborate for what: a structural topic model analysis on CDP data

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Collaborate for what: a structural topic model analysis on CDP data

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dc.contributor.author Salvatore, Camilla es_ES
dc.contributor.author Madonna, Alice es_ES
dc.contributor.author Bianchi, Annamaria es_ES
dc.contributor.author Boffelli, Albachiara es_ES
dc.contributor.author Kalchschmidt, Matteo es_ES
dc.date.accessioned 2022-11-10T12:48:05Z
dc.date.available 2022-11-10T12:48:05Z
dc.date.issued 2022-09-20
dc.identifier.isbn 9788413960180
dc.identifier.uri http://hdl.handle.net/10251/189568
dc.description.abstract [EN] The aim of this paper is to understand why firms engage with their suppliers to collaborate for sustainability. To this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two most prevalent reasons firms engage with their suppliers relate to several aspects of the management of the supply chain, and the services and goods mobility efficiency. It is further noted how first-tier suppliers do not dispose of established capabilities and, therefore, are still in the course of improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys. es_ES
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022)
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Sustainable supply chain management es_ES
dc.subject Carbon disclosure project es_ES
dc.subject Supplier collaboration es_ES
dc.subject Structural Topic Model es_ES
dc.subject Text mining es_ES
dc.title Collaborate for what: a structural topic model analysis on CDP data es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2022.2022.15074
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Salvatore, C.; Madonna, A.; Bianchi, A.; Boffelli, A.; Kalchschmidt, M. (2022). Collaborate for what: a structural topic model analysis on CDP data. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 139-146. https://doi.org/10.4995/CARMA2022.2022.15074 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Junio 29-Julio 01, 2022 es_ES
dc.relation.conferenceplace Valencia, España
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2022/paper/view/15074 es_ES
dc.description.upvformatpinicio 139 es_ES
dc.description.upvformatpfin 146 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\15074 es_ES


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