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Article: A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics

TitleA blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics
Authors
KeywordsUrban logistics
blockchain
customer satisfaction
machine learning
sustainability
Issue Date2020
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp
Citation
International Journal of Production Research, 2020, Epub 2020-08-25, p. 1-21 How to Cite?
AbstractThe rapid development of urbanisation and the ever-changing consumers’ demands are constantly changing the urban logistics industry, imposing challenges on logistics service providers to improve customer satisfaction which is one of the indicators for the sustainability of urban logistics. Existing customer satisfaction evaluations are based on a questionnaire survey, which is time-consuming and labour intensive. Moreover, the logistics data are confidential and can only be accessed by the stakeholders in existing logistics models, causing the problem of information non-transparency among logistics enterprises and the third authorities like banks and governments, which may hinder the sustainable development of urban logistics. In this paper, we propose a blockchain-based evaluation approach for customer satisfaction in the context of urban logistics. Four criteria affecting customer satisfaction in urban logistics are identified. A machine learning algorithm Long Short-Term Memory (LSTM) is adopted to predict customer satisfaction in the future period. The implementation is demonstrated to illustrate the proposed approach. A smart contract is designed for compensation and/or refund to customers when their satisfaction with the delivery services is at a low level.
Persistent Identifierhttp://hdl.handle.net/10722/286219
ISSN
2019 Impact Factor: 4.577
2015 SCImago Journal Rankings: 1.445

 

DC FieldValueLanguage
dc.contributor.authorTian, Z-
dc.contributor.authorZhong, RY-
dc.contributor.authorBarenji, AV-
dc.contributor.authorWang, YT-
dc.contributor.authorLi, Z-
dc.contributor.authorRong, YM-
dc.date.accessioned2020-08-31T07:00:50Z-
dc.date.available2020-08-31T07:00:50Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Production Research, 2020, Epub 2020-08-25, p. 1-21-
dc.identifier.issn0020-7543-
dc.identifier.urihttp://hdl.handle.net/10722/286219-
dc.description.abstractThe rapid development of urbanisation and the ever-changing consumers’ demands are constantly changing the urban logistics industry, imposing challenges on logistics service providers to improve customer satisfaction which is one of the indicators for the sustainability of urban logistics. Existing customer satisfaction evaluations are based on a questionnaire survey, which is time-consuming and labour intensive. Moreover, the logistics data are confidential and can only be accessed by the stakeholders in existing logistics models, causing the problem of information non-transparency among logistics enterprises and the third authorities like banks and governments, which may hinder the sustainable development of urban logistics. In this paper, we propose a blockchain-based evaluation approach for customer satisfaction in the context of urban logistics. Four criteria affecting customer satisfaction in urban logistics are identified. A machine learning algorithm Long Short-Term Memory (LSTM) is adopted to predict customer satisfaction in the future period. The implementation is demonstrated to illustrate the proposed approach. A smart contract is designed for compensation and/or refund to customers when their satisfaction with the delivery services is at a low level.-
dc.languageeng-
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp-
dc.relation.ispartofInternational Journal of Production Research-
dc.rightsAOM/Preprint Before Accepted: his article has been accepted for publication in [JOURNAL TITLE], published by Taylor & Francis. AOM/Preprint After Accepted: This is an [original manuscript / preprint] of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI].-
dc.subjectUrban logistics-
dc.subjectblockchain-
dc.subjectcustomer satisfaction-
dc.subjectmachine learning-
dc.subjectsustainability-
dc.titleA blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics-
dc.typeArticle-
dc.identifier.emailZhong, RY: zhongzry@hku.hk-
dc.identifier.authorityZhong, RY=rp02116-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00207543.2020.1809733-
dc.identifier.scopuseid_2-s2.0-85089862590-
dc.identifier.hkuros313775-
dc.identifier.volumeEpub 2020-08-25-
dc.identifier.spage1-
dc.identifier.epage21-
dc.publisher.placeUnited Kingdom-

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