Unstructured big data analytics for retrieving e-commerce logistics knowledge. Issue 1 (April 2018)
- Record Type:
- Journal Article
- Title:
- Unstructured big data analytics for retrieving e-commerce logistics knowledge. Issue 1 (April 2018)
- Main Title:
- Unstructured big data analytics for retrieving e-commerce logistics knowledge
- Authors:
- Wu, Pei-Ju
Lin, Kun-Chen - Abstract:
- Highlights: A hybrid unstructured big data analytics is proposed. Ten critical themes of e-commerce logistics are generated from topic mining. Two vital strategies of e-commerce logistics are created by RDT and IDT theories. Abstract: The divergent evolution of e-commerce has complicated its correspondingly logistics management. However, few studies have explored e-commerce logistics business models via big data analytics. Hence, this investigation explores e-commerce logistics business models from unstructured big data. Specifically, this work develops a hybrid content analytical model to scrutinize essential knowledge of e-commerce logistics. The empirical results of the proposed model incorporate theories of resource dependence theory (RDT) and innovation diffusion theory (IDT) to generate logistical strategies. Ten critical themes of e-commerce logistics from topic mining are "Southeast Asia's e-commerce logistics payments", "E-commerce order management", "E-commerce logistics cloud services", "E-commerce logistics package management", "Europe e-commerce trends", "India's e-commerce logistics", "E-commerce distribution management", "Tax policies", "E-commerce logistics platforms", and "E-commerce logistics networks". Moreover, the fundamental rule of "cross-border e-commerce logistics" is uncovered by the association rules model. The proposed hybrid content analytics framework provides a research foundation for e-commerce logistics management. Furthermore, e-commerceHighlights: A hybrid unstructured big data analytics is proposed. Ten critical themes of e-commerce logistics are generated from topic mining. Two vital strategies of e-commerce logistics are created by RDT and IDT theories. Abstract: The divergent evolution of e-commerce has complicated its correspondingly logistics management. However, few studies have explored e-commerce logistics business models via big data analytics. Hence, this investigation explores e-commerce logistics business models from unstructured big data. Specifically, this work develops a hybrid content analytical model to scrutinize essential knowledge of e-commerce logistics. The empirical results of the proposed model incorporate theories of resource dependence theory (RDT) and innovation diffusion theory (IDT) to generate logistical strategies. Ten critical themes of e-commerce logistics from topic mining are "Southeast Asia's e-commerce logistics payments", "E-commerce order management", "E-commerce logistics cloud services", "E-commerce logistics package management", "Europe e-commerce trends", "India's e-commerce logistics", "E-commerce distribution management", "Tax policies", "E-commerce logistics platforms", and "E-commerce logistics networks". Moreover, the fundamental rule of "cross-border e-commerce logistics" is uncovered by the association rules model. The proposed hybrid content analytics framework provides a research foundation for e-commerce logistics management. Furthermore, e-commerce logistics can be implemented by vital strategies: "Establish inter-organizational and technical collaboration to create positive operations performance" and "Comprehend law, policy, and cultural differences to customize appropriate technologies of e-commerce logistics". … (more)
- Is Part Of:
- Telematics and informatics. Volume 35:Issue 1(2018)
- Journal:
- Telematics and informatics
- Issue:
- Volume 35:Issue 1(2018)
- Issue Display:
- Volume 35, Issue 1 (2018)
- Year:
- 2018
- Volume:
- 35
- Issue:
- 1
- Issue Sort Value:
- 2018-0035-0001-0000
- Page Start:
- 237
- Page End:
- 244
- Publication Date:
- 2018-04
- Subjects:
- E-commerce -- Logistics management -- Big data analytics -- Content analytics -- Unstructured big data
Telecommunication -- Periodicals
Computer networks -- Periodicals
Télécommunications -- Périodiques
Réseaux d'ordinateurs -- Périodiques
384 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07365853 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.tele.2017.11.004 ↗
- Languages:
- English
- ISSNs:
- 0736-5853
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8782.955000
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British Library HMNTS - ELD Digital store - Ingest File:
- 5590.xml