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Lim, Xin-Jean, Chang, Jennifer Yee-Shan, Cheah, Jun-Hwa 
ORCID: https://orcid.org/0000-0001-8440-9564, Lim, Weng Marc, Kraus, Sascha and Dabić, Marina
(2024)
Out of the way, human! Understanding post-adoption of last-mile delivery robots.
Technological Forecasting and Social Change, 201.
ISSN 0040-1625
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Abstract
The pace of technological development is exceeding expectations and transforming the landscape of last-mile delivery. This study investigates how users' post-adoption behavior in using delivery robots is formed. Based on the task-technology fit (TTF) model, we present a research model that includes both direct and indirect factors that have been previously overlooked in the literature. We collected data from 550 users of delivery robots. Our structural equation modelling results show that two hedonic- (i.e., gratification and anthropomorphism) and three utilitarian- (i.e., service quality experience, delivery task requirements, and user-facing technology performance) driven factors predict perceived TTF in using delivery robots. Value-in-use and trust have sequential mediating effects that connect perceived TTF and service reuse likelihood and word-of-mouth recommendation. Our findings suggest ways to improve last-mile delivery robot strategies and provide practical implications for the industry.
| Item Type: | Article |
|---|---|
| Additional Information: | Acknowledgment: This research was financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research program P5–0441. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data availability: The data that has been used is confidential. |
| Faculty \ School: | Faculty of Social Sciences > Norwich Business School |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Marketing |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Mar 2024 18:30 |
| Last Modified: | 06 Jun 2024 08:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94469 |
| DOI: | 10.1016/j.techfore.2024.123242 |
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