A Unified Framework for Intelligent Logistics: Integrating Blockchain-AI Systems with Mobile Pedagogical Support.

Saved in:

Bibliographic Details
Title:	A Unified Framework for Intelligent Logistics: Integrating Blockchain-AI Systems with Mobile Pedagogical Support.
Authors:	Nozdreva, Irina¹ ienozdreva@fa.ru, Popova, Vera¹ vvpopova@fa.ru, Sivakova, Svetlana¹ syusivakova@fa.ru
Source:	International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 9, p31-42. 12p.
Subjects:	Logistics, System integration, Artificial intelligence, Small business, Supply chain management, Technology Acceptance Model, Mobile learning, Blockchains
Abstract:	This study looks at how combining blockchain-AI systems with mobile learning support can improve efficiency, technology acceptance, and learning in logistics. Over 12 months, 132 organizations and 396 learners took part in one of four groups: control, technology-only, mobile learning-only, or the integrated framework. Researchers used repeated-measures MANOVA, factorial ANOVA, mediation analysis, and qualitative thematic analysis to analyse the data. The integrated framework led to significant operational improvements (27.6%-84.2%), always beating the 25% performance benchmark. Technology acceptance was much higher in the integrated group (d = 0.82-1.02), and perceived usefulness accounted for 55.8% of the relationship between learning engagement and system adoption. A factorial ANOVA showed a 13.5% synergy effect, indicating that the combined approach performed better than the individual parts alone. This effect was strongest in SMEs. Qualitative results showed that simulation and just-in-time support helped build confidence. Overall, these findings support a socio-technical approach and show that integrating mobile learning into digital systems is key to realizing the full potential of new supply chain technologies. The study provides a scalable way for organizations to close the implementation gap in digital logistics. [ABSTRACT FROM AUTHOR]
	Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	This study looks at how combining blockchain-AI systems with mobile learning support can improve efficiency, technology acceptance, and learning in logistics. Over 12 months, 132 organizations and 396 learners took part in one of four groups: control, technology-only, mobile learning-only, or the integrated framework. Researchers used repeated-measures MANOVA, factorial ANOVA, mediation analysis, and qualitative thematic analysis to analyse the data. The integrated framework led to significant operational improvements (27.6%-84.2%), always beating the 25% performance benchmark. Technology acceptance was much higher in the integrated group (d = 0.82-1.02), and perceived usefulness accounted for 55.8% of the relationship between learning engagement and system adoption. A factorial ANOVA showed a 13.5% synergy effect, indicating that the combined approach performed better than the individual parts alone. This effect was strongest in SMEs. Qualitative results showed that simulation and just-in-time support helped build confidence. Overall, these findings support a socio-technical approach and show that integrating mobile learning into digital systems is key to realizing the full potential of new supply chain technologies. The study provides a scalable way for organizations to close the implementation gap in digital logistics. [ABSTRACT FROM AUTHOR]
ISSN:	18657923
DOI:	10.3991/ijim.v20i09.61495