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- Publisher Website: 10.1080/00207543.2020.1809733
- Scopus: eid_2-s2.0-85089862590
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Article: A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics
Title | A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics |
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Authors | |
Keywords | Urban logistics blockchain customer satisfaction machine learning sustainability |
Issue Date | 2020 |
Publisher | Taylor & 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? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/286219 |
ISSN | 2023 Impact Factor: 7.0 2023 SCImago Journal Rankings: 2.668 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Tian, Z | - |
dc.contributor.author | Zhong, RY | - |
dc.contributor.author | Barenji, AV | - |
dc.contributor.author | Wang, YT | - |
dc.contributor.author | Li, Z | - |
dc.contributor.author | Rong, YM | - |
dc.date.accessioned | 2020-08-31T07:00:50Z | - |
dc.date.available | 2020-08-31T07:00:50Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | International Journal of Production Research, 2020, Epub 2020-08-25, p. 1-21 | - |
dc.identifier.issn | 0020-7543 | - |
dc.identifier.uri | http://hdl.handle.net/10722/286219 | - |
dc.description.abstract | The 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.language | eng | - |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp | - |
dc.relation.ispartof | International Journal of Production Research | - |
dc.rights | AOM/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.subject | Urban logistics | - |
dc.subject | blockchain | - |
dc.subject | customer satisfaction | - |
dc.subject | machine learning | - |
dc.subject | sustainability | - |
dc.title | A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics | - |
dc.type | Article | - |
dc.identifier.email | Zhong, RY: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, RY=rp02116 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/00207543.2020.1809733 | - |
dc.identifier.scopus | eid_2-s2.0-85089862590 | - |
dc.identifier.hkuros | 313775 | - |
dc.identifier.volume | Epub 2020-08-25 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 21 | - |
dc.identifier.isi | WOS:000564290400001 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0020-7543 | - |