Academic Journal
Artificial intelligence in the management of transportation companies
| Τίτλος: | Artificial intelligence in the management of transportation companies |
|---|---|
| Συγγραφείς: | Ando, Marx Davidson Nkonyam, Zakrzewski, Bartosz, Dul, Irena |
| Στοιχεία εκδότη: | University of Piraeus. International Strategic Management Association |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Artificial intelligence, Transportation -- Management -- Data processing, Predictive analytics, Sustainability, Decision support systems, Mathematical optimization |
| Περιγραφή: | PURPOSE: This article examines the transformative impact of artificial intelligence (AI) on the management of transportation enterprises, with particular emphasis on enhancements in planning accuracy, operational efficiency, environmental sustainability, and strategic decision-making. As transport systems grow increasingly interconnected, dynamic, and datarich, AI provides new capabilities that fundamentally reshape managerial practices. ; DESIGN/METHODOLOGY/APPROACH: The study employs an integrative synthesis of contemporary scholarly literature to identify and organize the mechanisms through which AI influences managerial processes in the transport sector. Drawing from state-of-the-art research, a fourlayer conceptual framework comprising data acquisition, AI-driven analytics, decisionsupport functions, and operational execution is developed to systematize the multifaceted effects of AI on organizational management. ; FINDINGS: The analysis demonstrates that AI significantly enhances and strengthens predictive planning, route optimization, fleet maintenance scheduling, warehouse coordination, and risk mitigation. Beyond automating routine tasks, AI enhances managerial cognition by enabling rapid adaptation to uncertainty, supporting evidence-based decision making, and facilitating more environmentally responsible transport operations. These effects collectively contribute to the modernization and increased resilience of transportation companies. ; PRACTICAL IMPLICATIONS: The proposed conceptual model offers managers, practitioners, and policy makers a structured tool for understanding and implementing AI solutions within transportation organizations. The findings highlight practical pathways for leveraging AI to improve operational performance, strengthen strategic planning, and advance sustainability objectives across the transport sector. ; ORIGINALITY/VALUE: By integrating and synthesizing current research, this article provides a comprehensive and analytically grounded framework that elucidates the ... |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/146903 |
| DOI: | 10.35808/ersj/4321 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/146903 https://doi.org/10.35808/ersj/4321 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Αριθμός Καταχώρησης: | edsbas.7F90D979 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar/handle/123456789/146903# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
|---|---|
| Header | DbId: edsbas DbLabel: BASE An: edsbas.7F90D979 RelevancyScore: 1011 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1010.63568115234 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Artificial intelligence in the management of transportation companies – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ando%2C+Marx+Davidson+Nkonyam%22">Ando, Marx Davidson Nkonyam</searchLink><br /><searchLink fieldCode="AR" term="%22Zakrzewski%2C+Bartosz%22">Zakrzewski, Bartosz</searchLink><br /><searchLink fieldCode="AR" term="%22Dul%2C+Irena%22">Dul, Irena</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: University of Piraeus. International Strategic Management Association – Name: DatePubCY Label: Publication Year Group: Date Data: 2026 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Transportation+--+Management+--+Data+processing%22">Transportation -- Management -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+analytics%22">Predictive analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Description Group: Ab Data: PURPOSE: This article examines the transformative impact of artificial intelligence (AI) on the management of transportation enterprises, with particular emphasis on enhancements in planning accuracy, operational efficiency, environmental sustainability, and strategic decision-making. As transport systems grow increasingly interconnected, dynamic, and datarich, AI provides new capabilities that fundamentally reshape managerial practices. ; DESIGN/METHODOLOGY/APPROACH: The study employs an integrative synthesis of contemporary scholarly literature to identify and organize the mechanisms through which AI influences managerial processes in the transport sector. Drawing from state-of-the-art research, a fourlayer conceptual framework comprising data acquisition, AI-driven analytics, decisionsupport functions, and operational execution is developed to systematize the multifaceted effects of AI on organizational management. ; FINDINGS: The analysis demonstrates that AI significantly enhances and strengthens predictive planning, route optimization, fleet maintenance scheduling, warehouse coordination, and risk mitigation. Beyond automating routine tasks, AI enhances managerial cognition by enabling rapid adaptation to uncertainty, supporting evidence-based decision making, and facilitating more environmentally responsible transport operations. These effects collectively contribute to the modernization and increased resilience of transportation companies. ; PRACTICAL IMPLICATIONS: The proposed conceptual model offers managers, practitioners, and policy makers a structured tool for understanding and implementing AI solutions within transportation organizations. The findings highlight practical pathways for leveraging AI to improve operational performance, strengthen strategic planning, and advance sustainability objectives across the transport sector. ; ORIGINALITY/VALUE: By integrating and synthesizing current research, this article provides a comprehensive and analytically grounded framework that elucidates the ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://www.um.edu.mt/library/oar/handle/123456789/146903 – Name: DOI Label: DOI Group: ID Data: 10.35808/ersj/4321 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar/handle/123456789/146903<br />https://doi.org/10.35808/ersj/4321 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. – Name: AN Label: Accession Number Group: ID Data: edsbas.7F90D979 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.7F90D979 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.35808/ersj/4321 Languages: – Text: English Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Transportation -- Management -- Data processing Type: general – SubjectFull: Predictive analytics Type: general – SubjectFull: Sustainability Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Artificial intelligence in the management of transportation companies Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ando, Marx Davidson Nkonyam – PersonEntity: Name: NameFull: Zakrzewski, Bartosz – PersonEntity: Name: NameFull: Dul, Irena IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |