Abstract
While past studies have acknowledged that both tangible and intangible features reside in all goods and services, a persistent challenge for online service retailing resides in the difficultly of portraying services online due to the latter’s abstract nature. Drawing on visual rhetoric theory, we advance human facial cues and verbal anchoring as visual cues that can bolster the appeal of tangible elements embedded in the portal image of a given service, which in turn culminates in increased sales. We further examine how the effects of facial cue and verbal anchoring are moderated by consumers’ reliance on the tangible cues of the service. By employing computer vision and deep learning techniques to parse out portal images of over 299,000 local service offerings on a popular Chinese group buying site, we discovered that the presence of facial cues and the richness of verbal anchoring embedded in the portal image of a service offering significantly increase sales. Our results further illustrate that these effects are reinforced by consumers’ perceived importance of service tangibility.
Originalsprog | Engelsk |
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Titel | Proceedings of the 39th International Conference on Information Systems (ICIS) |
Antal sider | 17 |
Udgivelsessted | Atlanta, GA |
Forlag | Association for Information Systems. AIS Electronic Library (AISeL) |
Publikationsdato | 2018 |
ISBN (Elektronisk) | 9780996683173 |
Status | Udgivet - 2018 |
Begivenhed | The 39th International Conference on Information Systems. ICIS 2018: Bridging the Internet of People, Data, and Things - San Francisco, USA Varighed: 13 dec. 2018 → 16 dec. 2018 Konferencens nummer: 39 https://icis2018.aisconferences.org/ |
Konference
Konference | The 39th International Conference on Information Systems. ICIS 2018 |
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Nummer | 39 |
Land/Område | USA |
By | San Francisco |
Periode | 13/12/2018 → 16/12/2018 |
Internetadresse |
Navn | Proceedings of the International Conference on Information Systems |
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ISSN | 0000-0033 |
Emneord
- Service tangibility
- Visual rhetoric
- Deep learning
- Computer vision