Smart and Sustainable: Consumer Perceptions of AI-Based Green Logistics

Ruchir Shah (), Suraj Shah and Priyanka Pathak
Additional contact information
Ruchir Shah: Ganpat University - Centre for Management Studies and Research
Suraj Shah: Ganpat University - Centre for Management Studies and Research
Priyanka Pathak: Ganpat University - Centre for Management Studies and Research

A chapter in Proceedings of the 5th International Conference on Management Research (ICMR 2025), 2026, pp 529-565 from Springer

Abstract: Abstract There is a need to incorporate artificial intelligence based green logistics due to the increased reliance of e-commerce on environmental sustainability. This research measures consumer attitudes towards AI powered sustainable logistics through a structured questionnaire and provides advanced analysis using IBM SPSS Statistics v26 and AMOS v26. The regression analysis investigates the impact of environmental benefits, ease of use, and price sensitivity on their satisfaction. The results identify crucial issues that support adoption and innovative moves for business. The behavioral and technology adoption Rure Framework provides a fresh perspective on AI powered Green Logistics in e-commerce for e-commerce managers.

Keywords: AI-Based Sustainable Logistics; Consumer Trust in Green Logistics; Sustainability in Green Logistics (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-660-9_26

Ordering information: This item can be ordered from
http://www.springer.com/9789462396609

DOI: 10.2991/978-94-6239-660-9_26

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().