Investigating consumers’ usage intention of contactless logistics services: A learning-based score fusion approach to coping with uncertainty of perception

Tianyi Chen, Yiik Diew Wong, Kum Fai Yuen, Duowei Li and Xueqin Wang

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 189, issue C

Abstract: Contactless logistics services, including contactless shopping and delivery, have garnered great attention from the public, and questionnaire survey is an effective way to understand consumers’ intentions of using these services. However, previous studies have not paid much attention to the uncertainty of evaluation scores in such survey. To this end, this study aims to address the following two questions: first, what are the key factors contributing to the consumers’ usage intention; and second, how to cope with the uncertainty. Therefore, this study proposes a score fusion approach which can measure the uncertainty more precisely and better reflect consumers’ ground-truth attitude. The approach innovatively encapsulates the following three methods: 1. A multivariate fuzzification method to quantify the uncertainty of the elements in a reference set based on probability membership function; 2. A fusion method that fuses the information represented by continuous random variables in the context of Dempster-Shafer (D-S) theory; and 3. an evaluation criterion powered by machine learning (ML), which is employed to assess fuzzification and determine the parameters of the probability membership functions. In a case study, the approach is applied on a survey dataset of consumers’ intentions on using contactless logistics services in the COVID-19 pandemic. The results manifest that the uncertainty of the elements in a reference set with 9-point Likert scale may not be the same with each other. The approach outperforms several state-of-the-art methods on measuring uncertainty and reflecting consumers’ ground-truth attitude, providing a solid basis for better identifying the key factors. Several interesting findings regarding the key factors are interpreted, offering valuable insights to suppliers of contactless logistics services. These insights could help them formulate effective business strategies in the post-COVID-19 era.

Keywords: Uncertainty measure; Evaluation score fusion; Probability membership function; Dempster-Shafer theory; Machine learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1366554524002515
Full text for ScienceDirect subscribers only

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:eee:transe:v:189:y:2024:i:c:s1366554524002515

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/bibliographic
http://www.elsevier. ... 600244/bibliographic

DOI: 10.1016/j.tre.2024.103660

Access Statistics for this article

Transportation Research Part E: Logistics and Transportation Review is currently edited by W. Talley

More articles in Transportation Research Part E: Logistics and Transportation Review from Elsevier
Bibliographic data for series maintained by Catherine Liu ().