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A machine learning driven decision support system for evaluating port performance: development and validation

Leonardo Leoni, Xiaotian Xie, Guoqing Zhao Orcid Logo, Yi Wang, Filippo De Carlo

Journal of Decision Systems, Volume: 35, Issue: 1

Swansea University Author: Guoqing Zhao Orcid Logo

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DOI (Published version): 10.1080/12460125.2026.2682227

Abstract

Limited research has examined how ports’ big data analytics capability (BDAC) is associated with operational and sustainable performance. In response, this study develops and validates a decision support system (DSS) that integrates expert judgements, fuzzy set theory, unsupervised machine learning...

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Published in: Journal of Decision Systems
ISSN: 1246-0125 2116-7052
Published: Informa UK Limited 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71987
first_indexed 2026-05-27T14:19:20Z
last_indexed 2026-06-17T04:34:07Z
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spelling 2026-06-16T12:03:03.5222567 v2 71987 2026-05-27 A machine learning driven decision support system for evaluating port performance: development and validation 2ff29aa347835abe2af6d98fa89064b4 0009-0003-9537-9016 Guoqing Zhao Guoqing Zhao true false 2026-05-27 CBAE Limited research has examined how ports’ big data analytics capability (BDAC) is associated with operational and sustainable performance. In response, this study develops and validates a decision support system (DSS) that integrates expert judgements, fuzzy set theory, unsupervised machine learning (ML), Decision Trees, and Bayesian Network analysis. Data were collected through a Likert-scale questionnaire completed by 158 respondents from 40 major ports. The responses were aggregated using an improved Similarity Aggregation Method, and K-Means clustering was applied to classify ports into performance groups. Decision Trees were then developed to identify performance clusters and key improvement areas, while a Bayesian Network was used to explore relationships among BDAC, port operational performance, and port sustainable performance. The results indicate that ports with stronger BDAC generally achieve better operational and sustainable performance, although other contextual factors may also play important roles. Journal Article Journal of Decision Systems 35 1 Informa UK Limited 1246-0125 2116-7052 Big data analytics capability; port operations; port management; decision-making 5 6 2026 2026-06-05 10.1080/12460125.2026.2682227 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) 2026-06-16T12:03:03.5222567 2026-05-27T15:15:29.1688973 Faculty of Humanities and Social Sciences School of Management - Business Management Leonardo Leoni 1 Xiaotian Xie 2 Guoqing Zhao 0009-0003-9537-9016 3 Yi Wang 4 Filippo De Carlo 5 71987__36977__0a2e6c5d829f4e4dbf23e5fb4fe54d60.pdf 71987.VoR.pdf 2026-06-16T12:00:52.6742816 Output 3005129 application/pdf Version of Record true 2026 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title A machine learning driven decision support system for evaluating port performance: development and validation
spellingShingle A machine learning driven decision support system for evaluating port performance: development and validation
Guoqing Zhao
title_short A machine learning driven decision support system for evaluating port performance: development and validation
title_full A machine learning driven decision support system for evaluating port performance: development and validation
title_fullStr A machine learning driven decision support system for evaluating port performance: development and validation
title_full_unstemmed A machine learning driven decision support system for evaluating port performance: development and validation
title_sort A machine learning driven decision support system for evaluating port performance: development and validation
author_id_str_mv 2ff29aa347835abe2af6d98fa89064b4
author_id_fullname_str_mv 2ff29aa347835abe2af6d98fa89064b4_***_Guoqing Zhao
author Guoqing Zhao
author2 Leonardo Leoni
Xiaotian Xie
Guoqing Zhao
Yi Wang
Filippo De Carlo
format Journal article
container_title Journal of Decision Systems
container_volume 35
container_issue 1
publishDate 2026
institution Swansea University
issn 1246-0125
2116-7052
doi_str_mv 10.1080/12460125.2026.2682227
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description Limited research has examined how ports’ big data analytics capability (BDAC) is associated with operational and sustainable performance. In response, this study develops and validates a decision support system (DSS) that integrates expert judgements, fuzzy set theory, unsupervised machine learning (ML), Decision Trees, and Bayesian Network analysis. Data were collected through a Likert-scale questionnaire completed by 158 respondents from 40 major ports. The responses were aggregated using an improved Similarity Aggregation Method, and K-Means clustering was applied to classify ports into performance groups. Decision Trees were then developed to identify performance clusters and key improvement areas, while a Bayesian Network was used to explore relationships among BDAC, port operational performance, and port sustainable performance. The results indicate that ports with stronger BDAC generally achieve better operational and sustainable performance, although other contextual factors may also play important roles.
published_date 2026-06-05T06:02:44Z
_version_ 1868490881709899776
score 11.109323

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