Evaluating public acceptance of autonomous delivery robots during COVID-19 pandemic. (December 2020)
- Record Type:
- Journal Article
- Title:
- Evaluating public acceptance of autonomous delivery robots during COVID-19 pandemic. (December 2020)
- Main Title:
- Evaluating public acceptance of autonomous delivery robots during COVID-19 pandemic
- Authors:
- Pani, Agnivesh
Mishra, Sabya
Golias, Mihalis
Figliozzi, Miguel - Abstract:
- Highlights: Acceptance of delivery robots analyzed in the context of global spotlight on contactless deliveries. Contingent valuation method used to evaluate consumers' willingness to pay for delivery robots. Issue of consumer heterogeneity in acceptance is addressed using a latent class model. Attitude-based segmentation revealed six underlying consumer segments. Demographical and psychological determinants of willingness to pay are discussed. Abstract: Autonomous delivery robot (ADR) technology for last-mile freight deliveries is a valuable step towards low-carbon logistics. The ongoing COVID-19 pandemic has put a global spotlight on ADRs for contactless package deliveries, and tremendous market interest has been pushing ADR developers to provide large-scale operation in several US cities. The deployment and penetration of ADR technology in this emerging marketplace calls for collection and analysis of consumer preference data on ADRs. This study addresses the need for research on public acceptance of ADRs and offers a detailed analysis of consumer preferences, trust, attitudes, and willingness to pay (WTP) using a representative sample of 483 consumers in Portland. The results reveal six underlying consumer segments: Direct Shoppers, E-Shopping Lovers, COVID Converts, Omnichannel Consumers, E-Shopping Skeptics, and Indifferent Consumers. By identifying the WTP determinants of these latent classes, this study provides actionable guidance for fostering mass adoption ofHighlights: Acceptance of delivery robots analyzed in the context of global spotlight on contactless deliveries. Contingent valuation method used to evaluate consumers' willingness to pay for delivery robots. Issue of consumer heterogeneity in acceptance is addressed using a latent class model. Attitude-based segmentation revealed six underlying consumer segments. Demographical and psychological determinants of willingness to pay are discussed. Abstract: Autonomous delivery robot (ADR) technology for last-mile freight deliveries is a valuable step towards low-carbon logistics. The ongoing COVID-19 pandemic has put a global spotlight on ADRs for contactless package deliveries, and tremendous market interest has been pushing ADR developers to provide large-scale operation in several US cities. The deployment and penetration of ADR technology in this emerging marketplace calls for collection and analysis of consumer preference data on ADRs. This study addresses the need for research on public acceptance of ADRs and offers a detailed analysis of consumer preferences, trust, attitudes, and willingness to pay (WTP) using a representative sample of 483 consumers in Portland. The results reveal six underlying consumer segments: Direct Shoppers, E-Shopping Lovers, COVID Converts, Omnichannel Consumers, E-Shopping Skeptics, and Indifferent Consumers. By identifying the WTP determinants of these latent classes, this study provides actionable guidance for fostering mass adoption of low-carbon deliveries in the last-mile. … (more)
- Is Part Of:
- Transportation research. Volume 89(2020)
- Journal:
- Transportation research
- Issue:
- Volume 89(2020)
- Issue Display:
- Volume 89, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 89
- Issue:
- 2020
- Issue Sort Value:
- 2020-0089-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-12
- Subjects:
- Low-carbon delivery -- Consumer acceptance -- Attitude-based segmentation -- Willingness to pay -- Latent class analysis -- COVID-19
Transportation -- Research -- Periodicals
Transportation -- Environmental aspects -- Periodicals
354.76 - Journal URLs:
- http://www.sciencedirect.com/science/journal/13619209 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.trd.2020.102600 ↗
- Languages:
- English
- ISSNs:
- 1361-9209
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9026.274630
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