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dc.contributor.authorAhmed, Abdelrahman M.
dc.contributor.authorSivarajah, Uthayasankar
dc.contributor.authorIrani, Zahir
dc.contributor.authorMahroof, Kamran
dc.contributor.authorVincent, Charles
dc.date.accessioned2023-01-04T00:50:57Z
dc.date.accessioned2023-01-19T17:23:02Z
dc.date.available2023-01-04T00:50:57Z
dc.date.available2023-01-19T17:23:02Z
dc.date.issued2022-10
dc.identifier.citationAhmed AM, Sivarajah U, Irani Z, Mahroof K and Vincent C (2022) Data-driven subjective performance evaluation: An attentive deep neural networks model based on a call centre case. Annals of Operations Research. 333: 939–970en_US
dc.identifier.urihttp://hdl.handle.net/10454/19294
dc.descriptionYesen_US
dc.description.abstractEvery contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is characterised by subjectivity, which in turn creates a skewed picture of agent performance. Detecting and eliminating subjectivity is the study challenge that requires empirical research to address. In this paper, we introduce an evidence-based machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using a Deep Neural Network (DNN) for a multi-classification problem. The study draws the first baseline for subjectivity detection, achieving an accuracy of 75%, which is close to relevant speech studies in emotional recognition and performance classification. Among other findings, we conclude that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process, or subjective scores should be deducted from the overall results.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/s10479-022-04874-2en_US
dc.rights© The Authors 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.subjectSubjective evaluationen_US
dc.subjectAgent performanceen_US
dc.subjectCustomer behaviouren_US
dc.subjectDeep neural networken_US
dc.subjectCall centreen_US
dc.subjectCall quality monitoringen_US
dc.titleData-driven subjective performance evaluation: An attentive deep neural networks model based on a call centre caseen_US
dc.status.refereedYesen_US
dc.date.Accepted2022-07-15
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.rights.licenseCC-BYen_US
dc.date.updated2023-01-04T00:50:58Z
refterms.dateFOA2023-01-19T17:24:35Z
dc.openaccess.statusopenAccessen_US


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